{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XuXWJLEm2UWS"
   },
   "source": [
    "# **CS224W - Colab 0**\n",
    "\n",
    "Colab 0 **will not be graded**, so you don't need to hand in this notebook. That said, we highly recommend you to run this notebook, so you can get familiar with the basic concepts of graph mining and Graph Neural Networks.\n",
    "\n",
    "In this Colab, we will introduce two packages, [NetworkX](https://networkx.org/documentation/stable/) and [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/).\n",
    "\n",
    "For the PyTorch Geometric section, you don't need to understand all the details already. Concepts and implementations of graph neural network will be covered in future lectures and Colabs.\n",
    "\n",
    "Please make a copy before you proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AIUjFBAcdaHX"
   },
   "source": [
    "# New Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8gzsP50bF6Gb"
   },
   "source": [
    "\n",
    "# NetworkX Tutorial\n",
    "\n",
    "NetworkX is one of the most frequently used Python packages to create, manipulate, and mine graphs.\n",
    "\n",
    "Main parts of this tutorial are adapted from https://colab.research.google.com/github/jdwittenauer/ipython-notebooks/blob/master/notebooks/libraries/NetworkX.ipynb#scrollTo=zA1OO6huHeV6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nwwq0nSdmsOL"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "QeqN7MHvH1OA"
   },
   "outputs": [],
   "source": [
    "# Import the NetworkX package\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCIeGAfLfAMK"
   },
   "source": [
    "## Graph\n",
    "NetworkX provides several classes to store different types of graphs, such as directed and undirected graph. It also provides classes to create multigraphs (both directed and undirected).\n",
    "\n",
    "For more information, please refer to [NetworkX graph types](https://networkx.org/documentation/stable/reference/classes/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "qSI_n6P-e0PW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "{}\n",
      "True\n",
      "{'Name': 'Bar'}\n"
     ]
    }
   ],
   "source": [
    "# Create an undirected graph G\n",
    "G = nx.Graph()\n",
    "print(G.is_directed())\n",
    "\n",
    "#prw\n",
    "print(G.graph)\n",
    "\n",
    "# Create a directed graph H\n",
    "H = nx.DiGraph()\n",
    "print(H.is_directed())\n",
    "\n",
    "# Add graph level attribute\n",
    "G.graph[\"Name\"] = \"Bar\"\n",
    "print(G.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'networkx.classes.graph.Graph'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PRW\n",
    "H = nx.path_graph(10)\n",
    "print(type(H))\n",
    "nx.draw(H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x0pLs0-Ka9j8"
   },
   "source": [
    "## Node\n",
    "\n",
    "Nodes (with attributes) can be easily added to NetworkX graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "jQ8ApAL5H1GB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 0 has the attributes {'feature': 0, 'label': 0}\n"
     ]
    }
   ],
   "source": [
    "# Add one node with node level attributes\n",
    "G.add_node(0, feature=0, label=0)\n",
    "\n",
    "# Get attributes of the node 0\n",
    "node_0_attr = G.nodes[0]\n",
    "print(\"Node 0 has the attributes {}\".format(node_0_attr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "btOdMYqJaKia"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, {'feature': 0, 'label': 0})\n",
      "(1, {'feature': 1, 'label': 1})\n",
      "(2, {'feature': 2, 'label': 2})\n",
      "G has 3 nodes\n"
     ]
    }
   ],
   "source": [
    "# Add multiple nodes with attributes\n",
    "G.add_nodes_from([\n",
    "  (1, {\"feature\": 1, \"label\": 1}),\n",
    "  (2, {\"feature\": 2, \"label\": 2})\n",
    "])\n",
    "\n",
    "# Loop through all the nodes\n",
    "# Set data=True will return node attributes\n",
    "for node in G.nodes(data=True):\n",
    "  print(node)\n",
    "\n",
    "# Get number of nodes\n",
    "num_nodes = G.number_of_nodes()\n",
    "print(\"G has {} nodes\".format(num_nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0AdoaZPgbRis"
   },
   "source": [
    "## Edge\n",
    "\n",
    "Similar to nodes, edges (with attributes) can also be easily added to NetworkX graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "0szH5F6EH079"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge (0, 1) has the attributes {'weight': 0.5}\n"
     ]
    }
   ],
   "source": [
    "# Add one edge with edge weight 0.5\n",
    "G.add_edge(0, 1, weight=0.5)\n",
    "\n",
    "# Get attributes of the edge (0, 1)\n",
    "edge_0_1_attr = G.edges[(0, 1)]\n",
    "print(\"Edge (0, 1) has the attributes {}\".format(edge_0_1_attr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weight': 0.5}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PRW\n",
    "G.edges[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "QRTmi4EUaf_I"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1)\n",
      "(0, 2)\n",
      "(1, 2)\n",
      "G has 3 edges\n"
     ]
    }
   ],
   "source": [
    "# Add multiple edges with edge weights\n",
    "G.add_edges_from([\n",
    "  (1, 2, {\"weight\": 0.3}),\n",
    "  (2, 0, {\"weight\": 0.1})\n",
    "])\n",
    "\n",
    "# Loop through all the edges\n",
    "# Here there is no data=True, so only the edge will be returned\n",
    "for edge in G.edges():\n",
    "  print(edge)\n",
    "\n",
    "# Get number of edges\n",
    "num_edges = G.number_of_edges()\n",
    "print(\"G has {} edges\".format(num_edges))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9u1Utjn4bc7k"
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "lykPzFOEahuP"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the graph\n",
    "nx.draw(G, with_labels = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Q6YTP2FDbOS"
   },
   "source": [
    "## Node Degree and Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "GFA3B6Z_DE3q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 1 has degree 2\n",
      "Node 1 has neighbor 0\n",
      "Node 1 has neighbor 2\n"
     ]
    }
   ],
   "source": [
    "node_id = 1\n",
    "\n",
    "# Degree of node 1\n",
    "print(\"Node {} has degree {}\".format(node_id, G.degree[node_id]))\n",
    "\n",
    "# Get neighbor of node 1\n",
    "for neighbor in G.neighbors(node_id):\n",
    "  print(\"Node {} has neighbor {}\".format(node_id, neighbor))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4gVRVckZeSdA"
   },
   "source": [
    "## Other Functionalities\n",
    "\n",
    "NetworkX also provides plenty of useful methods to study graphs.\n",
    "\n",
    "Here is an example to get [PageRank](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_analysis.pagerank_alg.pagerank.html#networkx.algorithms.link_analysis.pagerank_alg.pagerank) of nodes (we will talk about PageRank in one of the future lectures)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "_gZfQ82Wiuvv"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.17857162031103999,\n",
       " 1: 0.32142837968896,\n",
       " 2: 0.32142837968896,\n",
       " 3: 0.17857162031103999}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAEuCAYAAADx63eqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAWwklEQVR4nO3df2zU933H8df3fuAzPy4OYLABL15wscMPZ4RocZo0GCrsjaSa1pKWDlamEKB1FK2YuIwQ7Y9p6RItS5QfGEiUSpHCtkhW1TQZa0haQNUq0hUyUoHPyFlgOL0ztolrTH3nu/N3f4AdH2cb/7jfn+fjP+6+99UbCeWd5919v2fZtm0LAABDONI9AAAAqcTiAwAYhcUHADAKiw8AYBQWHwDAKCw+AIBRWHwAAKOw+AAARmHxAQCMwuIDABiFxQcAMAqLDwBgFBYfAMAoLD4AgFFYfAAAo7D4AABGYfEBAIzC4gMAGIXFBwAwCosPAGAUFh8AwCgsPgCAUVzpHgAAkH06e0NqOtkmX6BHPcGIvB6XKoq8enjVIs2ZmZfu8cZk2bZtp3sIAEB2OH2xW/uOter4uQ5JUigyMPScx+WQLam6vFB1q8t0Z0lBmqYcG4sPADAub544r6cP+xSMRDXW5rAsyeNyau/6Cm2uKk3ZfOPFW50AgJu6tvSa1RceuOmxti31haN6+nCzJGXc8qP4AABjOn2xWxtfO6G+cDTm8WjfFXUdflHB8x/Jke/Vrau3aMay6phj8t1OvbW9SpWLMudtT77VCQAY075jrQpGonGPXz6yX5bTrUWPv6m5X3tCXUca1d9xIeaYYCSqxmOtqRp1XFh8AIBRdfaGdPxcR9xnegP9Qf2h5VcqeGCzHNPy5SlZpull9+jqmaMxx9m2dLSlQ129oRROPTYWHwBgVE0n20Z8PHL5M1kOp9yzFw495p73xwrfUHySZElqOjXyedKBxQcAGJUv0BNzycKggXCfrLz8mMccedM10N8Xd2wwMiCf/0rSZpwoFh8AYFQ9wciIjzvc+bJDsUvODv1Bjmn5Ix7fEwwnfLbJYvEBAEbl9Yx81Ztr9kLZA1GFL3829Fj/pU/lLrxtlPO4kzLfZLD4AACjqijyKs8Vvyoc0zyaXn6vun95SAP9QQXbzuoPrR9qxrI1ccd6XA5VFM9KxbjjwuIDAIxqw6pFoz43u6ZOdqRfbS9vUudP/1lzauo0bYTisyVtuGv086Qad24BAMTp6+vThQsXdODAAblDt6v/1sVxlzQ482dp3jeeGvM8liWtKS/MqBtXs/gAAJKkM2fO6Jvf/Kba2tp09epVRaPXLlp/491j+qcP++Lu3DIeHpdTddVliR51SnirEwAgSSouLlZbW5t6enoUjUblcDj0zDPP6DsPrtbe9RXKd09sZeS7Hdq7viKjblcmUXwAgOv8fr+cTufQn0tKSrRr1y5JX9xoOhd+nYHiAwBo586dqqys1JIlS/TQQw/JsiwdPHhQLtcXfbS5qlRvba9S7dL5ynM55Lnh254el0N5Lodql87XW9urMnLpSfw6AwAY7cyZM6qtrVVHR4caGxu1detWXb58WYcOHdLjjz8+6uu6ekNqOtUmn/+KeoJheT1uVRTP0oa7+AV2AECG2rlzp1566SXdfffdeu+991RQkFmfxSULn/EBgGGGV96rr76qrVu3pnuklOIzPgAwyOBneQsXLlR7e7txS0+i+ADACKZX3nAUHwDkuPr6euMrbziKDwByFJU3MooPAHIQlTc6ig8AcgiVd3MUHwDkCCpvfCg+AMhyVN7EUHwAkMWGV14gEGDpjQPFBwBZaHjlHTx4UI8++mi6R8oaFB8AZJkbK4+lNzEUHwBkCSovMSg+AMgCVF7iUHwAkMHOnj2r2tpaXbp0icpLEIoPADJUfX29VqxYoQULFlB5CUTxAUCGofKSi+IDgAwyWHnFxcVUXpJQfACQAZqbm1VTU0PlpQDFBwBpVl9fr+XLl6u4uFh+v5+ll2QUHwCkCZWXHhQfAKQBlZc+FB8ApNDwyjtw4IC2bduW7pGMQ/EBQIrcWHksvfSg+AAgyai8zELxAUASUXmZh+IDgCSg8jIXxQcACbZr1y4tX75cRUVFVF4GovgAIEGGV97+/fu1ffv2dI+EEVB8AJAAN1YeSy9zUXwAMAVUXvah+ABgkqi87ETxAcAENTc3q7a2Vu3t7VReFqL4AGACBitv/vz5VF6WovgAYByovNxB8QHATVB5uYXiA4BRUHm5ieIDgBE88cQTVF6OovgAYBgqL/dRfABwXUNDA5VnAIoPgPGoPLNQfACMRuWZh+IDYKTBygsEAlSeYSg+AMYZrLx58+YpEAiw9AxD8QEwxvDKa2xs1I4dO9I9EtKA4gNghBsrj6VnLooPQE7z+Xyqqamh8jCE4gOQsxoaGrRs2TIqDzEoPgA5p6WlRevWraPyMCKKD0BOaWho0NKlS6k8jIriA5ATqDyMF8UHIOtReZgIig9A1qLyMBkUH4CsROVhsig+AFmFysNUUXwAMlZXV5cCgcDQn6k8JIJl27ad7iEAmKGzN6Smk23yBXrUE4zI63Gposirh1ct0pyZeTHH2rat+++/X+3t7frJT36iBx98UH6/Xy+//DILD1PC4gOQdKcvdmvfsVYdP9chSQpFBoae87gcsiVVlxeqbnWZ7iwpkCS9++672rhxo0KhkCKRiFatWqUjR45o9uzZ6fgrIIew+AAk1Zsnzuvpwz4FI1GN9V8by5I8Lqf2rq/Qt1YtVElJidrb2yVJbrdbH374oVauXJmiqZHL+IwPQNJcW3rN6guPvfQkybalvnBUTx9u1v1/s0ft7e1yOByaNWuWJOn5559PwcQwAcUHIClOX+zWxtdOqC8cHXqs5+Q7uvrbn6u/47xm3LFacx/aOeJr7XBIZRfe0ZavrVFpaalKS0u1cOFCOZ3OVI2PHMblDACSYt+xVgUj0ZjHXDPn6JYvf0t9n56SHe4f9bUOd56+9BeP6Tub7072mDAQb3UCSLjO3pCOn+uIe3tzevmXNX3JvXLke8d8vS3paEuHunpDyRsSxmLxAUi4ppNtUz6HJanp1NTPA9yIxQcg4XyBnphLFiYjGBmQz38lQRMBX2DxAUi4nmAkQecJJ+Q8wHAsPgAJ5/Uk5ntzXo87IecBhmPxAUi4iiKv8lzx/3mxB6KyI/3SQFSyB2RH+mUPREc4w7U7ulQUz0r2qDAQ1/EBSLjO3pDue/YXcZ/zdf/ykH7/X/8W89gt931bBV/ZFHeOPJdDv9q9Nu4ensBUcR0fgISbOzNP95Z6dfyT7phLGgq+smnEJXcjy5LWlBey9JAULD4AU9bV1aXz58/r/Pnz+vWvf6033nhDC1fcK8+9O2Lu3DJeHpdTddVlSZgU4K1OAFP0wQcfaN26dfJ6vQoGg+rv75fH41FXV5d+/PGl6/fqHP+lDfluh/auv0Obq0qTNzSMxpdbAEzJ6tWrVVpaqp6eHvX398vtduvo0aOaPn26NleVau/6O5Tvdsqyxj6PZUn5bidLD0nHW50ApuSpp57ShQsXZFmWHA6HHnzwQVVVVQ09v7mqVJWLCtR4rFVHWzpk6drF6YMGf49vTXmh6qrLVLmoIPV/CRiFtzoBTEpLS4tqamrk9/v14osvyu/369lnn1Vra6tKSkpGfE1Xb0hNp9rk819RTzAsr8etiuJZ2nBX/C+wA8nC4gMwYbt379Zzzz2nlStX6mc/+5nmzp2rSCSiTz75ROXl5ekeDxgTiw/AuN1Yed/73vfSPRIwYXy5BcC47N69W0uXLlVhYaF+97vfsfSQtfhyC4AxDa+8V155hYWHrEfxARgVlYdcRPEBiEPlIZdRfABiUHnIdRQfAElUHsxB8QGg8mAUig8wGJUHE1F8gKGoPJiK4gMMQ+XBdBQfYBAqD6D4ACNQecAXKD4gx1F5QCyKD8hRVB4wMooPyEFUHjA6ig/IIVQecHMUH5AjqDxgfCg+IMtRecDEUHxAFqPygImj+IAsROUBk0fxAVmGygOmhuIDsgSVByQGxQdkASoPSByKD8hgVB6QeBQfkKGoPCA5KD4gw1B5QHJRfEAGofKA5KP4gAxA5QGpQ/EBaUblAalF8QFpQuUB6UHxAWlA5QHpQ/EBKUTlAelH8QEp8oMf/IDKAzIAxQckGZUHZBaKD0giKg/IPBQfkARUHpC5KD4gwag8ILNRfECCUHlAdqD4gASg8oDsQfEBU0DlAdmH4gMmicoDshPFB0wQlQdkN4oPmAAqD8h+FB8wDlQekDsoPuAmqDwgt1B8wCioPCA3UXzACKg8IHdRfMAwVB6Q+yg+4DoqDzADxQfjUXmAWSg+GG337t1UHmAYig9GovIAc1F8MA6VB5iN4oMxqDwAEsUHQ1B5AAZRfMhpVB6AG1F8yFlUHoCRUHzIOVQegLFQfMgpVB6Am6H4kBOoPADjRfEh61F5ACaC4kPWovIATAbFh6xE5QGYLBYfMt6pU6dUV1cn27bV0tKi2267TS+88IJeeeUV/eY3v9HcuXPTPSKALMJbnUi5zt6Qmk62yRfoUU8wIq/HpYoirx5etUhzZubFHGvbtrZs2SKfz6dPP/1UR44c0cqVK3Xy5EkWHoBJsWzbttM9BMxw+mK39h1r1fFzHZKkUGRg6DmPyyFbUnV5oepWl+nOkgJJ0qFDh7Rt2zb19fVJkn74wx9qz549KZ8dQO5g8SEl3jxxXk8f9ikYiWqsf3GWJXlcTu1dX6GvV87TnDlzFAwGrz9nae3atfrggw9SNDWAXMRnfEi6a0uvWX3hsZeeJNm21BeO6h//o1mVX39MwWBQ06ZN05IlS1RTU6O1a9emZmgAOYviQ1Kdvtitja+dUF84OvSYHQmr60ijguf/RwPBXrkKinTr6i3KX3x3zGudiur1by9XdeXtqR4bQA6j+JBU+461KhiJxjxmD0TlmjVXRX/1jEp2vqWCB/5aHW8/q0h3e8xxA5ZT//7x5VSOC8AALD4kTWdvSMfPdcS9vemY5lHBVzbJVTBfluXQ9LI/leuW+QoFWmOOs23paEuHunpDKZwaQK5j8SFpmk62jeu46NXPFb78maYV/lHcc5akplPjOw8AjAeLD0njC/TEXLIwEjsaUedPn9PMFV+Ve05J3PPByIB8/ivJGhGAgVh8SJqeYGTM5217QJ3v/ovkdGn2uu+OcZ5wokcDYDAWH5LG6xn9xkC2bavr8EuKXu1W4V8+Kcs5+rFejzsZ4wEwFIsPSVNR5FWea+R/Ypff26dw10XN2/D3crjzRjxGunZHl4riWckaEYCBuI4PSdPZG9J9z/4i7nO+yO8v6bP9j0hOtyyHc+jx2X/2mGYuWxNzbJ7LoV/tXht3D08AmCxuUo2kCV+5rDn97fI7CjX8/65ct8zTbX/37k1fb1nSmvJClh6AhOKtTiREX1+fnnzyST300ENaunSp8vLytGDBAs32/7fy3JP7Z+ZxOVVXXZbgSQGYjrc6kRBXr15VSUmJPv/886HHbr/9drW2turQhxeu36tz7Esbhst3O7R3/R3aXFWahGkBmIziQ0LMmDFD1dXVQ3/2eDx6++23ZVmWNleVau/6O5Tvdsqyxj6PZUn5bidLD0DSUHyYMp/Pp5qaGgUCAVVWVuqjjz7Sli1b9KMf/SjmuI/butV4rFVHWzpk6drF6YMGf49vTXmh6qrLVLmoILV/CQDGYPFhShoaGvT8889r5cqVOnLkiBwOhzZt2qQ33nhj1F9I7+oNqelUm3z+K+oJhuX1uFVRPEsb7or/BXYASDQWHyZleOW9/PLL2rFjR7pHAoBx4TM+TFhDQ4OWLVumefPmKRAIsPQAZBWu48O4Da+8xsZGFh6ArETxYVyoPAC5guLDmKg8ALmG4sOoqDwAuYjiQ5zm5mbV1tZSeQByEsWHGE888YSWL19O5QHIWRQfJFF5AMxB8YHKA2AUis9gVB4AE1F8hqLyAJiK4jMMlQfAdBSfQQYrb/78+VQeAGNRfAYYXnn79+/X9u3b0z0SAKQNxZfjbqw8lh4A01F8OWqw8trb26k8ABiG4stBu3btGqo8v9/P0gOAYSi+HELlAcDNUXw5gsoDgPGh+LIclQcAE0PxZTEqDwAmjuLLQs3NzaqpqaHyAGASKL4sM1h5RUVFXJcHAJNA8WUJKg8AEoPiywJUHgAkDsWXwQYr79KlS1QeACQIxZehhlce39gEgMSh+DIMlQcAyUXxZZD6+noqDwCSjOLLAFQeAKQOxZdmVB4ApBbFlyZUHgCkB8WXBoOVV1xcTOUBQIpRfCl09uxZ1dbW6tKlSzpw4IC2bduW7pEAwDgUX4rU19drxYoVQ5XH0gOA9KD4kozKA4DMQvElEZUHAJmH4kuCM2fOqLa2Vh0dHTp48KAeffTRdI8EALiO4kuw+vp6VVZWasGCBfL7/Sw9AMgwFF+CUHkAkB0ovgSg8gAge1B8U0DlAUD2ofgmabDyFi5cSOUBQBah+CaIygOA7EbxTcDwygsEAiw9AMhCFN84UHkAkDsovpvYuXPnUOW1t7ez9AAgy1F8o6DyACA3UXwjoPIAIHdRfMNQeQCQ+yi+66g8ADCD8cU3vPJeffVVbd26Nd0jAQCSyOjiu7HyWHoAkPuMLD4qDwDMZVzxUXkAYDZjio/KAwBIOVx8e/bs0caNG2Xbtr7//e9TeQAASZJl27ad7iHGo7M3pKaTbfIFetQTjMjrcamiyKuHVy3SnJl5Mcd+8sknWr58uWzb1owZM9Tb26vGxkYWHgAg8xff6Yvd2nesVcfPdUiSQpGBoec8LodsSdXlhapbXaY7SwokSbW1tXr//fdl27acTqdaWlq0ePHidIwPAMgwGb343jxxXk8f9ikYiWqsKS1L8ric2ru+QuHmo3rkkUeGnnM6ndqyZYtef/31FEwMAMh0GfvllmtLr1l94YGbHmvbUl84qn9454wuHfmx8vLydM8996iyslJLlizRAw88kIKJAQDZICOL7/TFbm187YT6wtGYxzvfeU7B86c1EA7KOeNWeau+oVl31sYcM80pNX33PlUuKkjlyACALJGRxbfvWKuCkWjc496qhzXnz/9WlsutcNdFBf51j6bNX6y8orKhY8IDUuOxVh3YfHcqRwYAZImMu5yhszek4+c6RvxMb1rhbbJc7ut/smTJUuRzf8wxti0dbelQV28o+cMCALJOxhVf08m2MZ/veq9RV3/7c9mRkKbNX6z8xfFlZ0lqOtWmHQ/wTU4AQKyMW3y+QE/MJQs3mlNbp9nrdij0mU/B//utLKc77phgZEA+/5VkjgkAyFIZ91ZnTzBy02Msh1OekmWKXunUlY8Oj3KecKJHAwDkgIxbfF7PBCJ0YCDuM74vzhNfggAAZNziqyjyKs8VP1b0areunj2ugf4+2QNR9f3vSV1tPi5P6Z/EHetxOVRRPCsV4wIAskzGfca3YdUivfDBufgnLEtXPvpPdb3XKNkDct0yT7d+dZumf+meuENtSRvuWpT8YQEAWSfjFt/cmXlavaRQ7ze3x1zS4Jx+i4o2PXPT11uWtKa8MO7G1QAASBn4VqckPVZdJo/LOanXelxO1VWX3fxAAICRMnLx3VlSoL3rK5Tvnth4+W6H9q6v4HZlAIBRZdxbnYM2V5VK0oR/nWHwdQAAjCQjb1I93Mdt3Wo81qqjLR2ydO3i9EGDv8e3prxQddVllB4A4KYyfvEN6uoNqelUm3z+K+oJhuX1uFVRPEsb7or/BXYAAEaTNYsPAIBEyMgvtwAAkCwsPgCAUVh8AACjsPgAAEZh8QEAjMLiAwAYhcUHADAKiw8AYBQWHwDAKCw+AIBRWHwAAKOw+AAARmHxAQCMwuIDABiFxQcAMAqLDwBgFBYfAMAoLD4AgFFYfAAAo7D4AABGYfEBAIzy//akYfHMY8HNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_nodes = 4\n",
    "# Create a new path like graph and change it to a directed graph\n",
    "G = nx.DiGraph(nx.path_graph(num_nodes))\n",
    "nx.draw(G, with_labels = True)\n",
    "\n",
    "# Get the PageRank\n",
    "pr = nx.pagerank(G, alpha=0.8)\n",
    "pr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RrIFCJnlvGkg"
   },
   "source": [
    "## Documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_7PBwhIKu3et"
   },
   "source": [
    "You can explore more NetworkX functions through its [documentation](https://networkx.org/documentation/stable/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MDcCjrW3JWzG"
   },
   "source": [
    "# PyTorch Geometric Tutorial\n",
    "\n",
    "PyTorch Geometric (PyG) is an extension library for PyTorch. It provides useful primitives to develop Graph Deep Learning models, including various graph neural network layers and a large number of benchmark datasets.\n",
    "\n",
    "Don't worry if you don't understand some concepts such as `GCNConv` -- we will cover all of them in the future lectures :)\n",
    "\n",
    "This tutorial is adapted from https://colab.research.google.com/drive/1h3-vJGRVloF5zStxL5I0rSy4ZUPNsjy8?usp=sharing#scrollTo=ci-LpZWhRJoI by [Matthias Fey](https://rusty1s.github.io/#/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "WU7NCZtQ6msa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch has version 1.7.0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(\"PyTorch has version {}\".format(torch.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HLxnaKsN8GVf"
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "qywlcjyr8USw"
   },
   "outputs": [],
   "source": [
    "# Helper function for visualization.\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualization function for NX graph or PyTorch tensor\n",
    "def visualize(h, color, epoch=None, loss=None):\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    if torch.is_tensor(h):  #is_tensor(obj): Returns True if obj is a PyTorch tensor.\n",
    "        h = h.detach().cpu().numpy()\n",
    "        #https://pytorch.org/docs/stable/autograd.html?highlight=detach#torch.Tensor.detach\n",
    "        #pytorch .detach() .detach_() 和 .data用于切断反向传播https://blog.csdn.net/weixin_33913332/article/details/93300411\n",
    "        \n",
    "        #https://pytorch.org/docs/stable/tensors.html?highlight=cpu#torch.Tensor.cpu\n",
    "        #cpu():Returns a copy of this object in CPU memory.\n",
    "        \n",
    "        #https://pytorch.org/docs/stable/tensors.html?highlight=numpy#torch.Tensor.numpy\n",
    "        #Returns self tensor as a NumPy ndarray.\n",
    "        \n",
    "        plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap=\"Set2\")\n",
    "        if epoch is not None and loss is not None:\n",
    "            plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)\n",
    "    else:\n",
    "        nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,\n",
    "                         node_color=color, cmap=\"Set2\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bbny-iTO7NQN"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community.\n",
    "Here, **Graph Neural Networks (GNNs)** aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.\n",
    "\n",
    "This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the **[PyTorch Geometric (PyG) library](https://github.com/rusty1s/pytorch_geometric)**.\n",
    "PyTorch Geometric is an extension library to the popular deep learning framework [PyTorch](https://pytorch.org/), and consists of various methods and utilities to ease the implementation of Graph Neural Networks.\n",
    "\n",
    "Following [Kipf et al. (2017)](https://arxiv.org/abs/1609.02907), let's dive into the world of GNNs by looking at a simple graph-structured example, the well-known [**Zachary's karate club network**](https://en.wikipedia.org/wiki/Zachary%27s_karate_club). This graph describes a social network of 34 members of a karate club and documents links between members who interacted outside the club. Here, we are interested in detecting communities that arise from the member's interaction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g3uPffzbyqn9"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "PyTorch Geometric provides an easy access to the dataset via the [`torch_geometric.datasets`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets) subpackage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "YrpL9CtS7nx2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: KarateClub():\n",
      "======================\n",
      "Number of graphs: 1\n",
      "Number of features: 34\n",
      "Number of classes: 4\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.datasets import KarateClub\n",
    "\n",
    "dataset = KarateClub()\n",
    "print(f'Dataset: {dataset}:')\n",
    "print('======================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch_geometric.datasets.karate.KarateClub"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PRW\n",
    "type(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lCeRGa2q7sdl"
   },
   "source": [
    "After initializing the [`KarateClub`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.KarateClub) dataset, we first can inspect some of its properties.\n",
    "For example, we can see that this dataset holds exactly **one graph**, and that each node in this dataset is assigned a **34-dimensional feature vector** (which uniquely describes the members of the karate club).\n",
    "Furthermore, the graph holds exactly **4 classes**, which represent the community each node belongs to.\n",
    "\n",
    "Let's now look at the underlying graph in more detail:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch_geometric.data.data.Data"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PRW\n",
    "type(dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "sTLapYhP7uCn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(edge_index=[2, 156], train_mask=[34], x=[34, 34], y=[34])\n",
      "==============================================================\n",
      "Number of nodes: 34\n",
      "Number of edges: 78\n",
      "Average node degree: 4.59\n",
      "Number of training nodes: 4\n",
      "Training node label rate: 0.12\n",
      "Contains isolated nodes: False\n",
      "Contains self-loops: False\n",
      "Is undirected: True\n"
     ]
    }
   ],
   "source": [
    "data = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print(data)\n",
    "print('==============================================================')\n",
    "\n",
    "# Gather some statistics about the graph.\n",
    "print(f'Number of nodes: {data.num_nodes}')\n",
    "print(f'Number of edges: {int(data.num_edges/2)}')  #原代码没有/2，但是我觉得不应当\n",
    "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')  #PRW:2E/N\n",
    "print(f'Number of training nodes: {data.train_mask.sum()}')\n",
    "print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\n",
    "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
    "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
    "print(f'Is undirected: {data.is_undirected()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lIzbIoc-y8J4"
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I5zhmKIH72Rf"
   },
   "source": [
    "Each graph in PyTorch Geometric is represented by a single [`Data`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data) object, which holds all the information to describe its graph representation.\n",
    "We can print the data object anytime via `print(data)` to receive a short summary about its attributes and their shapes:\n",
    "```\n",
    "Data(edge_index=[2, 156], x=[34, 34], y=[34], train_mask=[34])\n",
    "```\n",
    "We can see that this `data` object holds 4 attributes:\n",
    "(1) The `edge_index` property holds the information about the **graph connectivity**, *i.e.*, a tuple of source and destination node indices for each edge.\n",
    "PyG further refers to (2) **node features** as `x` (each of the 34 nodes is assigned a 34-dim feature vector), and to (3) **node labels** as `y` (each node is assigned to exactly one class).\n",
    "(4) There also exists an additional attribute called `train_mask`, which describes for which nodes we already know their community assigments.\n",
    "In total, we are only aware of the ground-truth labels of 4 nodes (one for each community), and the task is to infer the community assignment for the remaining nodes.\n",
    "\n",
    "The `data` object also provides some **utility functions** to infer some basic properties of the underlying graph.\n",
    "For example, we can easily infer whether there exists isolated nodes in the graph (*i.e.* there exists no edge to any node), whether the graph contains self-loops (*i.e.*, $(v, v) \\in \\mathcal{E}$), or whether the graph is undirected (*i.e.*, for each edge $(v, w) \\in \\mathcal{E}$ there also exists the edge $(w, v) \\in \\mathcal{E}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  1,\n",
      "          1,  1,  1,  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,\n",
      "          3,  3,  3,  3,  3,  4,  4,  4,  5,  5,  5,  5,  6,  6,  6,  6,  7,  7,\n",
      "          7,  7,  8,  8,  8,  8,  8,  9,  9, 10, 10, 10, 11, 12, 12, 13, 13, 13,\n",
      "         13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20, 20, 21,\n",
      "         21, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 27, 27,\n",
      "         27, 27, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, 31, 31,\n",
      "         31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33,\n",
      "         33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33],\n",
      "        [ 1,  2,  3,  4,  5,  6,  7,  8, 10, 11, 12, 13, 17, 19, 21, 31,  0,  2,\n",
      "          3,  7, 13, 17, 19, 21, 30,  0,  1,  3,  7,  8,  9, 13, 27, 28, 32,  0,\n",
      "          1,  2,  7, 12, 13,  0,  6, 10,  0,  6, 10, 16,  0,  4,  5, 16,  0,  1,\n",
      "          2,  3,  0,  2, 30, 32, 33,  2, 33,  0,  4,  5,  0,  0,  3,  0,  1,  2,\n",
      "          3, 33, 32, 33, 32, 33,  5,  6,  0,  1, 32, 33,  0,  1, 33, 32, 33,  0,\n",
      "          1, 32, 33, 25, 27, 29, 32, 33, 25, 27, 31, 23, 24, 31, 29, 33,  2, 23,\n",
      "         24, 33,  2, 31, 33, 23, 26, 32, 33,  1,  8, 32, 33,  0, 24, 25, 28, 32,\n",
      "         33,  2,  8, 14, 15, 18, 20, 22, 23, 29, 30, 31, 33,  8,  9, 13, 14, 15,\n",
      "         18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32]])\n"
     ]
    }
   ],
   "source": [
    "#PRW\n",
    "print(data.edge_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ True, False, False, False,  True, False, False, False,  True, False,\n",
      "        False, False, False, False, False, False, False, False, False, False,\n",
      "        False, False, False,  True, False, False, False, False, False, False,\n",
      "        False, False, False, False])\n"
     ]
    }
   ],
   "source": [
    "#PRW\n",
    "print(data.train_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 1., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 1.,  ..., 0., 0., 0.],\n",
      "        ...,\n",
      "        [0., 0., 0.,  ..., 1., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 1., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 1.]])\n"
     ]
    }
   ],
   "source": [
    "#PRW\n",
    "print(data.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0, 0, 0, 0, 2, 2, 2, 0, 3, 3, 2, 0, 0, 0, 3, 3, 2, 0, 3, 0, 3, 0, 3, 1,\n",
      "        1, 1, 3, 1, 1, 3, 3, 1, 3, 3])\n"
     ]
    }
   ],
   "source": [
    "#PRW\n",
    "print(data.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "iFA6Xi4O79r0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0,  1],\n",
      "        [ 0,  2],\n",
      "        [ 0,  3],\n",
      "        [ 0,  4],\n",
      "        [ 0,  5],\n",
      "        [ 0,  6],\n",
      "        [ 0,  7],\n",
      "        [ 0,  8],\n",
      "        [ 0, 10],\n",
      "        [ 0, 11],\n",
      "        [ 0, 12],\n",
      "        [ 0, 13],\n",
      "        [ 0, 17],\n",
      "        [ 0, 19],\n",
      "        [ 0, 21],\n",
      "        [ 0, 31],\n",
      "        [ 1,  0],\n",
      "        [ 1,  2],\n",
      "        [ 1,  3],\n",
      "        [ 1,  7],\n",
      "        [ 1, 13],\n",
      "        [ 1, 17],\n",
      "        [ 1, 19],\n",
      "        [ 1, 21],\n",
      "        [ 1, 30],\n",
      "        [ 2,  0],\n",
      "        [ 2,  1],\n",
      "        [ 2,  3],\n",
      "        [ 2,  7],\n",
      "        [ 2,  8],\n",
      "        [ 2,  9],\n",
      "        [ 2, 13],\n",
      "        [ 2, 27],\n",
      "        [ 2, 28],\n",
      "        [ 2, 32],\n",
      "        [ 3,  0],\n",
      "        [ 3,  1],\n",
      "        [ 3,  2],\n",
      "        [ 3,  7],\n",
      "        [ 3, 12],\n",
      "        [ 3, 13],\n",
      "        [ 4,  0],\n",
      "        [ 4,  6],\n",
      "        [ 4, 10],\n",
      "        [ 5,  0],\n",
      "        [ 5,  6],\n",
      "        [ 5, 10],\n",
      "        [ 5, 16],\n",
      "        [ 6,  0],\n",
      "        [ 6,  4],\n",
      "        [ 6,  5],\n",
      "        [ 6, 16],\n",
      "        [ 7,  0],\n",
      "        [ 7,  1],\n",
      "        [ 7,  2],\n",
      "        [ 7,  3],\n",
      "        [ 8,  0],\n",
      "        [ 8,  2],\n",
      "        [ 8, 30],\n",
      "        [ 8, 32],\n",
      "        [ 8, 33],\n",
      "        [ 9,  2],\n",
      "        [ 9, 33],\n",
      "        [10,  0],\n",
      "        [10,  4],\n",
      "        [10,  5],\n",
      "        [11,  0],\n",
      "        [12,  0],\n",
      "        [12,  3],\n",
      "        [13,  0],\n",
      "        [13,  1],\n",
      "        [13,  2],\n",
      "        [13,  3],\n",
      "        [13, 33],\n",
      "        [14, 32],\n",
      "        [14, 33],\n",
      "        [15, 32],\n",
      "        [15, 33],\n",
      "        [16,  5],\n",
      "        [16,  6],\n",
      "        [17,  0],\n",
      "        [17,  1],\n",
      "        [18, 32],\n",
      "        [18, 33],\n",
      "        [19,  0],\n",
      "        [19,  1],\n",
      "        [19, 33],\n",
      "        [20, 32],\n",
      "        [20, 33],\n",
      "        [21,  0],\n",
      "        [21,  1],\n",
      "        [22, 32],\n",
      "        [22, 33],\n",
      "        [23, 25],\n",
      "        [23, 27],\n",
      "        [23, 29],\n",
      "        [23, 32],\n",
      "        [23, 33],\n",
      "        [24, 25],\n",
      "        [24, 27],\n",
      "        [24, 31],\n",
      "        [25, 23],\n",
      "        [25, 24],\n",
      "        [25, 31],\n",
      "        [26, 29],\n",
      "        [26, 33],\n",
      "        [27,  2],\n",
      "        [27, 23],\n",
      "        [27, 24],\n",
      "        [27, 33],\n",
      "        [28,  2],\n",
      "        [28, 31],\n",
      "        [28, 33],\n",
      "        [29, 23],\n",
      "        [29, 26],\n",
      "        [29, 32],\n",
      "        [29, 33],\n",
      "        [30,  1],\n",
      "        [30,  8],\n",
      "        [30, 32],\n",
      "        [30, 33],\n",
      "        [31,  0],\n",
      "        [31, 24],\n",
      "        [31, 25],\n",
      "        [31, 28],\n",
      "        [31, 32],\n",
      "        [31, 33],\n",
      "        [32,  2],\n",
      "        [32,  8],\n",
      "        [32, 14],\n",
      "        [32, 15],\n",
      "        [32, 18],\n",
      "        [32, 20],\n",
      "        [32, 22],\n",
      "        [32, 23],\n",
      "        [32, 29],\n",
      "        [32, 30],\n",
      "        [32, 31],\n",
      "        [32, 33],\n",
      "        [33,  8],\n",
      "        [33,  9],\n",
      "        [33, 13],\n",
      "        [33, 14],\n",
      "        [33, 15],\n",
      "        [33, 18],\n",
      "        [33, 19],\n",
      "        [33, 20],\n",
      "        [33, 22],\n",
      "        [33, 23],\n",
      "        [33, 26],\n",
      "        [33, 27],\n",
      "        [33, 28],\n",
      "        [33, 29],\n",
      "        [33, 30],\n",
      "        [33, 31],\n",
      "        [33, 32]])\n"
     ]
    }
   ],
   "source": [
    "edge_index = data.edge_index\n",
    "print(edge_index.t())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nLLsT0ROzffp"
   },
   "source": [
    "## Edge Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wQJyi9OB8dh_"
   },
   "source": [
    "By printing `edge_index`, we can further understand how PyG represents graph connectivity internally.\n",
    "We can see that for each edge, `edge_index` holds a tuple of two node indices, where the first value describes the node index of the source node and the second value describes the node index of the destination node of an edge.\n",
    "\n",
    "This representation is known as the **COO format (coordinate format)** commonly used for representing sparse matrices.\n",
    "Instead of holding the adjacency information in a dense representation $\\mathbf{A} \\in \\{ 0, 1 \\}^{|\\mathcal{V}| \\times |\\mathcal{V}|}$, PyG represents graphs sparsely, which refers to only holding the coordinates/values for which entries in $\\mathbf{A}$ are non-zero.\n",
    "\n",
    "We can further visualize the graph by converting it to the `networkx` library format, which implements, in addition to graph manipulation functionalities, powerful tools for visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "3KfJHtlV8h3W"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "G = to_networkx(data, to_undirected=True)\n",
    "visualize(G, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUdHZY2u8vn3"
   },
   "source": [
    "## Implementing Graph Neural Networks\n",
    "\n",
    "After learning about PyG's data handling, it's time to implement our first Graph Neural Network!\n",
    "\n",
    "For this, we will use one of the most simple GNN operators, the **GCN layer** ([Kipf et al. (2017)](https://arxiv.org/abs/1609.02907)).\n",
    "\n",
    "PyG implements this layer via [`GCNConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv), which can be executed by passing in the node feature representation `x` and the COO graph connectivity representation `edge_index`.\n",
    "\n",
    "With this, we are ready to create our first Graph Neural Network by defining our network architecture in a `torch.nn.Module` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "6tQGQF8r8zIr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GCNConv(34, 4)\n",
      "  (conv2): GCNConv(4, 4)\n",
      "  (conv3): GCNConv(4, 2)\n",
      "  (classifier): Linear(in_features=2, out_features=4, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.nn import Linear\n",
    "from torch_geometric.nn import GCNConv\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GCNConv(dataset.num_features, 4)\n",
    "        self.conv2 = GCNConv(4, 4)\n",
    "        self.conv3 = GCNConv(4, 2)\n",
    "        self.classifier = Linear(2, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        h = self.conv1(x, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv2(h, edge_index)\n",
    "        h = h.tanh()\n",
    "        h = self.conv3(h, edge_index)\n",
    "        h = h.tanh()  # Final GNN embedding space.\n",
    "        \n",
    "        # Apply a final (linear) classifier.\n",
    "        out = self.classifier(h)\n",
    "\n",
    "        return out, h\n",
    "\n",
    "model = GCN()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5zgbaD5P8_M_"
   },
   "source": [
    "Here, we first initialize all of our building blocks in `__init__` and define the computation flow of our network in `forward`.\n",
    "We first define and stack **three graph convolution layers**, which corresponds to aggregating 3-hop neighborhood information around each node (all nodes up to 3 \"hops\" away).\n",
    "In addition, the `GCNConv` layers reduce the node feature dimensionality to $2$, *i.e.*, $34 \\rightarrow 4 \\rightarrow 4 \\rightarrow 2$. Each `GCNConv` layer is enhanced by a [tanh](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html?highlight=tanh#torch.nn.Tanh) non-linearity.\n",
    "\n",
    "After that, we apply a single linear transformation ([`torch.nn.Linear`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear)) that acts as a classifier to map our nodes to 1 out of the 4 classes/communities.\n",
    "\n",
    "We return both the output of the final classifier as well as the final node embeddings produced by our GNN.\n",
    "We proceed to initialize our final model via `GCN()`, and printing our model produces a summary of all its used sub-modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "48uhs_0j9AMX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding shape: [34, 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = GCN()\n",
    "\n",
    "_, h = model(data.x, data.edge_index)\n",
    "print(f'Embedding shape: {list(h.shape)}')\n",
    "\n",
    "visualize(h, color=data.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nDtJ9Zjw9I_Y"
   },
   "source": [
    "Remarkably, even before training the weights of our model, the model produces an embedding of nodes that closely resembles the community-structure of the graph.\n",
    "Nodes of the same color (community) are already closely clustered together in the embedding space, although the weights of our model are initialized **completely at random** and we have not yet performed any training so far!\n",
    "This leads to the conclusion that GNNs introduce a strong inductive bias, leading to similar embeddings for nodes that are close to each other in the input graph.\n",
    "\n",
    "### Training on the Karate Club Network\n",
    "\n",
    "But can we do better? Let's look at an example on how to train our network parameters based on the knowledge of the community assignments of 4 nodes in the graph (one for each community):\n",
    "\n",
    "Since everything in our model is differentiable and parameterized, we can add some labels, train the model and observe how the embeddings react.\n",
    "Here, we make use of a semi-supervised or transductive learning procedure: We simply train against one node per class, but are allowed to make use of the complete input graph data.\n",
    "\n",
    "Training our model is very similar to any other PyTorch model.\n",
    "In addition to defining our network architecture, we define a loss critertion (here, [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)) and initialize a stochastic gradient optimizer (here, [`Adam`](https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam)).\n",
    "After that, we perform multiple rounds of optimization, where each round consists of a forward and backward pass to compute the gradients of our model parameters w.r.t. to the loss derived from the forward pass.\n",
    "If you are not new to PyTorch, this scheme should appear familar to you. \n",
    "Otherwise, the PyTorch docs provide [a good introduction on how to train a neural network in PyTorch](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a-loss-function-and-optimizer).\n",
    "\n",
    "Note that our semi-supervised learning scenario is achieved by the following line:\n",
    "```\n",
    "loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
    "```\n",
    "While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**.\n",
    "Here, this is implemented by filtering the output of the classifier `out` and ground-truth labels `data.y` to only contain the nodes in the `train_mask`.\n",
    "\n",
    "Let us now start training and see how our node embeddings evolve over time (best experienced by explicitely running the code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "FI3DETGi9ND6",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oCEZEFpZIJLfdljEQnbCM6zjcvqUG150+ZIyBdWsqFS4zoIARkQWlrroU/5YzxyYXcR3W1Jff8vzdO1ayrCyOM8VgvzFBd9q772mcUa1LnQJGRBaUeMxlc1NV1t1kZSVR6leU3vJ8NOry8fdtoaGmjIhrbllfNOJQtayI/+v92ygtjt6yvExPYzAisuDcc0cDpy/2TjvYH3EN77p77aSzLRfFInzs0c109Azx2ol22roGsRaqK4vYtbWOlTWlmpp/FhQwIjKvrLWMjIyQTCaJRCIUFxdPu1NfVhbno49s4v987xSe599yyrJjgmlhHt7XSGPDsmlrqFlewsP3Nc3mbUgGChgRmRe+73P58mXOnj3L8PAwjuPg+z6xWIz169ezdu3aG1O8ZFJXXcrPP7aDN0538OqJa4yMemDAdQzbN6zgzq21LK8oyuM7kolMLvdR2LNnjz18+HCI5YjIUpBKpXj55Zfp7+/POM2+4ziUlJSwb9++rOb9staSTPn4viUec9WtlUfGmCPW2j2ZXtMgv4jk3ZEjR+jr65v0Hi6+7zM4OMjBgwezupmYMYZY1KUoHlG4FBAFjIjkVV9fH93d3fj+rTfyGs9ay+DgIB0dHXmqTOaaAkZE8qqlpWXacBnjeR5nz54NuSIJiwJGRPImkUjQ1taWVbfXmN7e3hArkjDpLDIRyYvu7m4OHTo06bjLZHIJIyksChgRmTPJZJLLly9z/vx5RkdHMcZQWVnJypUrOX78eM7hArp75EKmgBGROdHR0cHYZQzjg6Sjo2PGA/WO49DYqHnAFioFjIjMWldXF6+88krWg/e5WLt27ZyvU/JDg/wiMiupVIrDhw/Pebg4jsOdd95JPB6fvrEUJB3BiMiM9ff389JLL5FKpeZ0vZFIhF27dlFbWzun65X80hGMiMzI4OBgKOHiOA533HGHwmURUMCIyIwcP358zsMFgmlfampq5ny9kn8KGBHJ2cjICJ2dnXO+XmMMq1evnnIWZVk4FDAikrOOjo45n1TSGENRURFbtmyZ0/XK/NEgv4jkLJlMzukV9q7rUlZWxt69e4lGdXvixUIBIyI5i0ajc3IEU1JSQmVlJevWraOysnIOKpNCooARkZzV1tbO6gjGdV0aGxvZtm3bHFYlhUZjMCKSs3g8PqMzvYwxuK7LmjVr2Lp1awiVSSHREYyIzMhtt91Gd3c3yWQyq/au61JfX8+6detYtmxZyNVJIVDAiMiMFBcXc99993Hw4EGSyeQtMyW7rks8Hueee+6huLhYtzJeghQwIjJjZWVlvOtd76K9vZ2zZ88yODgIQHl5OevWraO2tlbBsoQpYERkVhzHob6+nvr6+vkuRQqMBvlFRCQUChgREQmFAkZEREKhgBERkVAoYEREJBQKGBERCYUCRkREQqGAERGRUChgREQkFAoYEREJhQJGRERCoYAREZFQaLJLkQVmcDjJ0EiSaMRlWVlMsxVLwVLAiCwA1lpOX+jh0BttdPUO47oG34dY1GH3tjp2bq4hHtM/Zyks+osUKXC+b/mn585yobWfZMoHwPMtACnP58Drrbx2sp2Pv3cLFWXx+SxV5CYagxEpcD84eIHzV/puhMtEKc8yOJzkiafeIpnyMrYRmQ8KGJECdn0wwYmWLlKenbKdtTA8muKtcz15qkxkegoYkQL2+lvtMHW23JBM+bxy7Gq4BYnkQAEjUsAuXr1+Y7wlGz39o3h+5q40kXxTwIgUMM/LLSyMAX+a7jSRfFHAiBSwirJYTu1dxxCJ6J+1FAb9JYoUsNs31xLNMjCMga3rqnXhpRQMBYxIAWtsqCAec7Nq6zqGXdvqQq5IJHsKGJECZozhsXdvnPYoJuI6vGP3aqori/NUmcj0FDAiBa6mqoSfft9WqpYVEYk4jO8Bi0Yc4jGXh+5dy66tOnqRwqKpYkQWgBXLi/nkh26jrXOQE2e7GBxKEI26rFtTyfo1y3AdfVeUwqOAEVlA6leUUr+idL7LEMmKvvaIiEgoFDAiIhIKBYyIiIRCASMiIqFQwIiISCgUMCIiEgoFjIiIhEIBIyIioVDAiIhIKBQwIiISCgWMiIiEQgEjIiKhUMCIiEgoFDAiIhIKBYyIiIRCASMiIqFQwIiISCgUMCIiEgoFjIiIhEIBIyIioVDAiIhIKBQwIiISCgWMiIiEIjLfBYhMZWhoiHPnztHa2ornebiuS319Pc3NzZSVlc13eSIyBQWMzDnfWvoSwyR9j7JonJJILOd1WGs5deoUZ8+exVqLtRaAVCrFxYsXuXTpEmvXrmX79u0YYzIu393dTVdXF57nUVRURENDA/F4fNbvT0Syo4CROTOcSvBC2xmevnySYS+JgyFlfdZXrOCR1dvYvnxlxjDI5NSpU7S0tOD7/i2vjQXOpUuXALjttttuev3q1aucOHGCRCKB53kAOI7Dm2++SU1NDTt37lTQiOSBAkbmROfIAF95/WkGUwmSvnfTa6f62jl/vYu7ahp5fOPdONOEzPDwMGfPns0YLuN5nsfFixdpbGykvLwcgIsXL3Ls2LFblh37ub29neeff579+/crZERCpkF+mbURL8l/ff379CVGbgmXMQnf45WOC/z9+denXd+FCxey3rbv+5w7dw6A/v7+jOEynrWWRCLB4cOHs96GiMyMAkZm7cC1cwymRrHYKdslfI8ftr7FYDIxZbvW1tZpj17Gu3r1KsCN8ZrpWGvp6+vj+vXrWW9DRHKngJFZsdbyvcsnSExy5DKRAV66dnbKNqlUKqcaPM9jYGCA1tbWrAIGgiOfXI6URCR3ChiZlWEvSV9iOOv2Cd/jeE/rlG0ikdyGBq21PPfcc1mHy5iBgYGc2otIbhQwMitJ38Mxuf0ZJafp/mpoaMBxsl/n+NOYc5HtGW0iMjMKGJmVkkgMP8ede1W8ZMrXGxsbZ1NSVhzHYfny5aFvR2QpU8DIrEQdl9urVpHtsUDcibB/5cYp2xQXF7NhwwZc1519gVPIR5CJLGUKGJm1h9dsJepkFwblsSI2VNRM227jxo2sX78ex3Fu6S4zxtx4zITrurqqXyQPFDAya83lK3jPqi3EpgmZIjfCL2/bn1UwGGPYtGkTDz74IM3NzRQVFRGJRIjH4zQ1NbFp06acxmnGuK7L8uXL2blzZ87LikhudCX/AtQ9OshgMkHcjbCiqGzaK+Pz4QNNt1MSjfOP538MwKgfnGpsCLrRlsdL+NTWd9JQWpnTeouLi9m6dStbt2696fnLly/nXGM0GmXz5s00NjZqgF8kDxQwC4RnfQ61n+epSyfoGh3ENQ6+9Slyo7xn9Rb212+kKBKd1xofWrWF/fUbONx5kTe6rjDqp6iKl3Jf/TqayqrndKdeVVWV05ljxhjuu+8+zcAskkcKmAUg6Xv8j2PPcu56540LGpME/034Hv944Q2eu3qGX739PSyLFc9nqcTcCPvq1rGvbl2o2ykpKaGyspLu7u6s2ldWVipcRPJMYzALwJ+ffJGWceEyUdL36B4Z5L/9+Pt4OUyxstBt27Ytq3EYx3HYtm1bHioSkfEUMAWubaiPYz1XJ51EcoyPpXd0mKNduY9NLFSVlZXs2bMH13Uzdr8ZY3Bdl927d+uaF5F5oIApcD+48lbWRyWjforvXj4RckWFpba2lvvvv5+mpiZc171xWrPrujQ2NrJ//37q6urmu0yRJUljMAXurb5r+NPMUjze5cGeEKspTCUlJWzfvp2tW7eSSAQzNcdisRmdxiwic0cBU4D6EyP8qO0ML7SdoXt0KKdlvfS8XEvxNFzHcSgqKprvMkQkTQFTYA61n+cbp18GC0mb3RT445VGYksyXESk8ChgCshrnZf4xumXpx3Qn0zEOLyzfsMcVyUiMjPqpC4Qnu/zjdMHZxwuEJw1dX/D1BNJiojkiwKmQLzefRlvBvc0GRN1XB7fsJeqeOkcViUiMnPqIisQr3RcYNTL7VbBAHE3QsxxeXzj3dxRvTqEykREZkYBUyAGk4mcl9ldvZZ765vZvryhICa8FBEZTwFTIMqiud+bZGNlLTuqVoVQjYjI7GkMpkDcXduEm+NRyDOtb4VUjYjI7ClgCsSOqoacl+kcGWAolXvXmohIPihgCoRjHGqLK3JeJjGDEwNERPJBAVNAGkqW5dTetz4lkVhI1YiIzI4CpoDcV7+OuJv9eRebl9URy6G9LC6e59Hd3U1HRwd9fX053eFTJB+0dyogWytXUuRGs7oeJua4PLxaN9FaioaHhzlz5gyXL1++Me+ctZZoNMq6detoamrSTNJSEPRXWEAcY/ilre8g5rhTtos5LnfVNLKlUvc5WWr6+/t5/vnnuXjxIp7nkUqlSKVSeJ7HyMgIJ0+e5MCBA3jezKccEpkrOoLJo7Fp+F+61sJgKkHMcdlRtYqHVm2mPj3+sr6ihs/seBd/dPx5kta76Wgmalwslv0rN/Lh5js1a/ISk0wmOXDgAMlkctI2vu/T19fHa6+9xp49e/JYncitFDB58sLV0/zvlldvmoZ/CHix7SwH289xV00jj2/ci2sc1lfU8Dv3PMYb3a08f/U0vaPDxByX26oa2L9yIxUx3fNkKbp06RJ+Fnc39X2f9vZ2hoaGKCkpyUNlIpkpYPLgxbYzPNHyasaZkn0svu9xuOMCKd/j5zfvC+4lbxzuqF6t+cUECMZYWlpasu76stZy7tw5tm/fHnJlIpPTGEzIRlJJ/vbsERLTTMOf8D2Odl3hbH9HniqThcT3fUZGRrJub62lp2fp3T5bCosCJmQH28+R7UhJ0k/xvSsnQ61HFqaZ3AZbpy3LfFPAhOxg+7lpj17GWOBY95VwC5IFyXXdnE89Li3VvYFkfilgQpbrXGG+taRmcVdLWZyMMaxduzbroxjXdWlubg65KpGpKWBCVpzjVC5jA/wiEzU1NWUVMMYYiouLqayszENVIpPTnixke2uapr1wcrztlSt1fYtkVFpayu233z5lV5kxhkgkwt69e/V3JPNOAROyfXXNZDvUGnMiPLx6a6j1yMK2atUq9uzZQ0lJCa779hcXYwyO41BdXc3+/ft1/YsUBF0HE7LiSIwPN9/Jk+dem3KwP+q4bF++ko3LavNYnSxEtbW1PPjgg/T09NDZ2UkqlaKoqIiVK1dSXFw83+WJ3KCAyYMHGzbh+T5/f+Eo1kLKvn01toMh4jjsqFrFz2++V90akhVjDFVVVVRVVc13KSKTUsDkyUOrt7C7Zi3Ptp7iQHouMoDKWDGbK+t4z+qtRHIYqxERKXQKmDxaHi9hTdlyXrxmcR2HUS9F5+gg3dfOcajjAg0ly/iFLfdRW1w+36WKiMyaBvnz6IWrp/nLUwe5nhy9aZZkH0vS97g40M1vvvYUbUP981iliMjcUMDkSdtQP/+75dUpB/otMOIl+R/Hn9U0HyKy4Clg8uT7V07iZTHVugWuJ0Y41dceflEiIiFSwOSBtZaD7efws7wiZtRP8ezVUyFXJSISLgVMHox6KXw7/dHLeJ0jgyFVIyKSHwqYPHAdBz/HMZWIrocRkQVOAZMHUcelKp791OkR47BpWV2IFYmIhE/XwUyjd3SIF6+10DrUh4Ohubyae+qaKclxluSHV2/lW9NMFzPeAw2bZlKuiEjBUMBMYjiV5OunDnCsuxV4e3qXo12XePL8Ud5Rv56PrtuV9dT699Q288+XjpFMeFMO9Ucdl90r1rI8rskKRWRhUxdZBiNekt8++l2OdbeSsv5Nc4clfI+k7/Fi21m+duL5rMdWiiJRPrvzPZRHi4hMEkoxx2XTslp+duPeOXkfIiLzSQGTwRNnj9A5MnBTsEyU8D3e6r3Gc1dPZ73e2uJyvrj7fTy8eislbpS4G6HYjRJ1XFaWVPAzG+7i322/X3OSiciioC6yCYZTSQ51XJgyXMYkfI/vXj7BAys3Zj0Lclm0iA823c771+6gbbifhJeiLFpETXHZbEsXESkoCpgJjnZdwiH7U4SHkgkuDHTTVF6d03Zcx2FVqW5pKyKLl7rIJugZHSLhp6ZvmGaMoXd0KMSKREQWJgXMBBHHxcnhIkeTXkZERG6mgJlgXXk1rsk+MJK+x9qy5SFWJCKyMClgJlhfUUN5NJ51+y9NPT8AABDhSURBVG3LV1IR033QRUQmUsBMYIzhI813Es2i2yvmuHygcWceqpJ8SiaTdHR0cPXqVbq7u3VvHpEZ0llkGeyqWUtfYpgnzx8l6d965b2DIeI4/NLWd7JG3WOLxtDQEKdOnaK1tRXHefu7lzGG5uZm1q9fj+tqvE0kWwqYSTy4ajNN5dU8dfkEx7pbiTgOFvCt5e6aJh5evZW6kor5LlPmSF9fHwcOHCCVCs4g9CfcHO7MmTO0tbWxb98+IhH9sxHJhv6lTKG5YgWf3rafoVSC3tEhjDFUx0uJufrYFpNEIsHBgwdvhEsmvu8zMDDAkSNHuPvuu/NYncjCpT1lFkoisZxnT5aF49KlS7ccsWTi+z5dXV0MDAxQVqaZF0Smo0F+WfJaWlrwvOxuo2Ct5dy5cyFXJLI4KGBkSfM8j9HR0azbW2vp7e0NsSKRxUMBI0tethOVjtFpyyLZUcDIkua6bs6nHmv8RSQ7ChhZ8hobG2+67mUqruvS3NwcckUii4MCRpa8pqamrLrJjDGUlJRQWanbLIhkQwEjS15xcTG7du2a8ijGGEM0GmXv3r05j9mILFUKGBGgrq6Ou+++m/LychzHuREijuPgOA61tbXs37+f4mJNbCqSLV1oKZJWXV3N/fffT39/P52dnXieRywWo76+nng8+xm2RSSggBGZoKKigooKzTMnMlvqIhMRkVAoYEREJBTqIstCf2KY3sQwEeNQU1ye1c3IRESWOgXMFI51t/LUpeOcu95FxHEBi7VwX/163rN6C1Xx0vkuUUSkYClgMrDW8kTLq/yo7QwJP5hlN+W9PZ37c1dP8dK1Fj6z40Gay1fMV5kiIgVNYzAZfO/ymzeFy0SetYx4SX7vjWfoHh3Mc3UiIgvDgjmCSXgpXum4wDOtp+hLDBNxHDYtq+WhVVtZU7Z8zraT9D2+c+nYpOFyc9sUT18+yU+t3z1n2xcRWSwWRMCc7G3jaydewFrLqP/2bW0PtV/g1c5LbFpWxy9tfcec3Mr4SMdFyHI2ds9aXmw7y08236GBfxGRCQq+i+xMXzt/ePw5RrzkTeEC4GNJ+B4ne9v4/WPP4Nnpb3s7nVN97bdsZ0oGOoavz3q7IiKLTUEHjLWWP3vrpWm7q1LW5+JAN4faz896mymb3a1zxxgMqTkINhGRxaagA+ZUXztDyURWbRO+x3cvvznrbdbEy3BN9h9LyveojGkCRBGRiQo6YA51nM+pu6pj+Dq9o0Oz2uY9devIZTL2pvJqKhQwIiK3KOiAuZ4Yyam9MYbXOi/Tlxie8TZristYX1GDm0XMxByX967ZPuNtiYgsZgUdMKXR3KZIT/oe3z5/lM8d+ge+euwZLg50z2i7v7BlH2WxoilDJua4vKN+A7dVNcxoGyIii11BB8yuFWuI53jq8aifImV9jvdc5SuvP83rXZdz3m5FrJjP3/koGytriRjnpjGZuBsh7kZ4/9odfGzdrpzXLSKyVBT0dTDbl68k5riMejmcNjxOwvf4s5Mv8rk7H2VlybKclq2IFfP/7Hg3nSMDHLjWQvvwAFHHYVNlHbtXrNV1LyIi0yjogHGMwyc23sOfnvwRySyurM8k5fs8dek4/3rzvhktv6KojJ9o3DmjZUVElrKC7iID2Fm9ip/beA9RxyVqcj9q8LEc6bzESCoZQnUiIjKZgj6CGXNXbSObK2t5oe0Mz189Q19iONvZXABwjUPHyMCczlkmIiJTK/gjmDEVsWL+1dodfPnux7i7tjnn5W1OkSQiIrO1YAJmvFUly3LqLkv5HlXxkhArEhGRiRZkwNxTty6nI5ItlfWURYtCrEhERCZakAFTESvijurVRLI4itHV9iIi82NeAsa3lrP9HRzpuMiPu67QP4OpXX52093UFZcRnWJiyqjj8oHGnWxYVjObckVEZAbyehaZZ31+cOUtnr78JqN+CkMw3X3S99i2fCUfarqdVaWVWa2ryI3yn+54hCdaXuVg+zkcDAk/hWMMEeNSGo3xkeZd7K5ZG+6bEhGRjIy12Y9l7Nmzxx4+fHhGG0r6Hl899gznr3dNen+XmOPyb7fdz9bl9TmteySV5GjXZbpHh4g4Dk3l1WysqMGYXOZFFhGRXBljjlhr92R6LW9HMH9z+hXOXe+a8or8hO/xtRPP84Xd72NFUVnW6y6KRLmnLvdTl0VEJDx5GYPpT4xwqON8VtO9pKzHD668lYeqREQkTHkJmBfbzmKyvI2XZy0vtp0lNcO5x0REpDDkJWDOXe8kmcO97i2W3lncNExEROZfXgLGz3GaFoMhh3MPRESkAOUlYFaVVN50067p+FiWxXTlvYjIQpaXgHnnyg1ZjsCAg2FX9RpiOd7JUkRECkteAmZFURmbK+uIZHEU4zoOD6/ZmoeqREQkTHmbKuYXttxHVbxkypCJOi4/vX4Pq0t13xYRkYUubwFTEonxuTvfy97aRqKOS9yJ4GCIGIeY41JXXM6ntr6T++rX56skEREJUV4HOoojUX5u0718bN0ejnZdoi8xTMRx2VBRQ1N5dT5LERGRkM3LSHpxJMq9devmY9MiIpInC/J+MCIiUvgUMCIiEgoFjIiIhEIBIyIioVDAiIhIKBQwIiISCgWMiIiEQgEjIiKhUMCIiEgoFDAiIhIKBYyIiIRCASMiIqFQwIiISCgUMCIiEgpjrc2+sTEdwIXwyhERkQWm0Vpbk+mFnAJGREQkW+oiExGRUChgREQkFAoYEREJhQJmATPGfNIYYyd59M5zbV83xlwOcf3rjDFD6fe6IcPr7zDGvGSMGTbGtBljftcYUzzDbT2Q3s5Ds688v2bzORhjHjTG/Ci9bLcx5pvGmLoM7bYbY540xrQaYwaNMceNMZ81xkSmWPfH05/plH8jxph9xhg/3XbS9Ulh0i9scfgoMPEfamo+CsmjPwL6gFt2lsaYncDTwHeB9wPNwFeAVcBP5bHGeTWbz8EY807ge+llPwxUA18CfmCM2W2tHU23awCeBa4AnwE6gXcDvwPUAP8xw7orgd8D2qapIQr8CXANqM/iLUuBUcAsDkettWfmu4h8Mcb8DHAn8FvAf8/Q5NcJAvej1tpkepkE8JfGmC9ba1/NW7HzazafwxcJLkn4kLU2lV72TeAV4P8mCHgIgmsFcJ+19lT6uR8aY9YDnyBDwBCEz+vAVWCqo8L/ABjgL4DPTfNepQCpi2wJGNeVtt8Y8/fGmAFjTJcx5g8ndpcYY1YaY75hjOk0xowaY35sjHk8wzqb010mbel2LcaY38/Q7k5jzAvp7qzTxphPzfK9LAd+F/gscEs3YPpb76PAE2M71bQngATwwdlsf5raHjXGHEh3KfWlP+vNE9o8ku6y6kv/Ht4yxnxh3OubjDHfNsa0G2NGjDEXjTF/l2v30Bx8DvcAT4+FC4C19jDQBTw2rl0s/d/+Ccv3kmH/Yoy5D3gc+OVp6l8PfB74t0ByqrZSuBQwi4NrjIlMeGT63f4VcAb4SYJv/r8IfG3sRWNMKfAc8F6Cb4wfAt4AvmmM+Tfj2jUDh4D9wBcIdmS/TvBNdrwK4G/S2/0gwbffrxljHhzfKB1+X8/yvf4OcNJa+81JXl8PFAHHxj9prR0BzgLbstxOTowxjwLfAQYIup8+DdwG/MgYsyrdZh3wj8C5dJsPEIRl6bhVfYegC+vTwCPAfwJGGfdv1Rhz3hjz7DQlzfZz8AiCaKLR9Psa83cE3WJ/kP7SUWGMeQz4WeC/jV8wHXp/CnwliyPuPwb+zlr7/DTtpJBZa/VYoA/gk4Cd5PFPGdr98YTl/zPBjmRT+ud/l273wIR23wfaATf98zcIdqQNU9T29fS6Hhz3XJzgG/CfTmibAv48i/f7ToId3LYJ72vDuDb70s89mmH5HwE/mMHn/EB6nQ9N0eYwcBqIjHuumeDb9++mf/5Iej0Vk6xjRfr1D0xTz5np3sdsPweCLxAvT3iuEfCB0QnPbwCOj/vb84EvZFjn59O1F437G7mcod3jQDdQm/7519LrjUxVsx6F99AYzOLwGLcO8mc6i+yJCT//L4KB273AKYIjkivW2mcntPsr4H8SfOt9A3iYIMBap6lryFr7zNgP1tpRY8wpYO34Rtbaaf8OjTExggHf/26tPTFd+3xKH/ntAn7T3tyldM4Y8yJwf/qpowSB87+MMX8BPG+tbR+3qi6gBfjt9Nlaz1prT0/cnrX2lrPmQvD7wF8ZY74EfBWoIjj68NMPAIwxNcCTwCBBgHYB7wI+b4wZtdZ+Od1uA8EXmsdscBSVkTGmiuCo7nMTPhtZgNRFtjgcs9YenvDI1AVxbZKfV6X/W0Uw8DpR27jXITijKJtTkHsyPDdK0HWTq88Ay4GvGmMq02cilaRfKzfGlE/Y5vIM66gi+GY815YTDEZP9tlVAaR/J48Q/Lv7JtBmjDlojLk//boF3kNwNPRbwKn02NanZ1DTrD4Ha+1fE3z5+PcEfycnCM4U+2dufp+/CjQBj1hrv2WtfdZa+wWCs9V+wxgz1m36VeCHwMFxv78YYNI/j40Ffim9/ifGtRv7e1mWDnNZIBQwS8vEaxjGfr6S/m83mU8HrR/3OgR97qsytAvTtnQdVwh2nj3AH6ZfexV4If3/ZwlCbPv4hY0xRcA6gh3lXOsh6MKZ7LO7sTO31j5jrX0UqCQ4gyoFfGdsR2ytbbHWfoLgFN87CXbKf2SMeW+ONc36c7DW/heCbrudwEpr7U8DGwm62MbsAM5Yayd+mTgERAm6zyD4/b2Pt393PcBPAw3p//+tce12EhwJjbUbOxOtE/jr6eqWwqGAWVo+NuHnjxN0d7yc/vk5YHX6TJ/xfoZgDGZsp/Q94P3GmJVhFZrBbwMPTnh8Of3a48AvAFhrE8BTwMcmnHn1EYIxoH+c68KstYPAEeCjxhh37HljTCPBWMizGZYZtdb+kOCkhVKC8Zrxr1tr7VHg/00/dRs5mKvPwVo7aK19w1p7LX0iwxaCAfgxbcCG9Nl9492d/u/Yl5ePc+vv77sEofEg8Afpdp/J0O4v0689RDCOIwvFfA8C6THzB28Pcn+E4LTSiY/IhHYXCbou3kPQH54A/ue49ZUSjMVcI9hhP0rQlWOBfzOuXRPQQXA21C8S7AQeB/5qXJuvk3kA91mCsYXxz2U1yD/F+98w4fk7gBGCsYF3E1y30U1wVtL4dg+kl//kNNsZa/fr6c96/OND6TaPEpww8S/ATxB8Oz+V/pwa0m0+RXBW3eME4zIfJugOu0JwwehO4Jl0u4cIutP+lmDcZve4eqYd5M/xc/hE+ndw/7jn7iQ4k/DR9ONLBEdEX56w7D3p+l4h+ALzbuA30n9bT05TX8a/kQztfg0N8i/Ix7wXoMcsfnlTn0VmgRUT2u0H/oHgDLBugi6m4gnrXEkQKp3pHcqPgcczbHt9eufXmd6JnSV9tlT69Yw7DzIHjAW+Pov3vyHDa/uBA+narhFcOV4yoc2/YpIzrSa0e2CKz3hgXLtH09scJphl4B+AzeNevzf93KX0Z3uV4DTfzenXawm+rZ8ChtK/o+cIxjfG13N+4mc4Re3ZfA5jn+MD457bTtAV1pt+P68C/3qSbdzD22MzgwRnlH1+4t9WhuUy/o1kaPdrKGAW5EP3g1kCjDGfJDgLbKNdQlf8T8cY85sE16LssPqHIDLnNAYjS9n9BKcWK1xEQqDrYGTJstZOPJlBROaQushERCQU6iITEZFQKGBERCQUChgREQmFAkZEREKhgBERkVD8/2c5NbFdcXdvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOMJsyAMwxNjamiYkJGWNUWVmpWCy23CWtSAQMAKScP39eJ0+e1NjY2OV7rgRBoPr6erW1tam2tnaZK1xZCBgAq561Vh0dHTpz5szlu0cGQXD59d7eXg0MDGjfvn1qbm5erjJXHM7BAFj1urq6rgiXdJLJpF544QUNDQ3lsbKVjYABsKpZa3XixIkFw2VGEARq7ziu831jOt83ponJRB4qXLnoIgOwqvX29mYULhPxEg1MVOrkgKfnu09IMkoGgbY01ei2fRu0vr7CfbErDAEDYNWamppSe3v7Fedb0hmcKFP/eJWsjCSj6fjr7V85M6Tuc8O65+Bm7dlen/b9QWBljGSMyWX5BY+AAbBqBEGg3t5eDQ0NaXR0VL29vbLWLvieselS9Y9Xp8IlvUTS6uGnulVdGdXG9dWSpL7BcR3puKgTXa8pkQwDpqmhUrfsXa8tTTXyvOIPGwIGQNGz1urVV1/VyZMnlUwmFz1ima1/vHLBcJmRSFp9/8hZvfu+Kj35/Fkd6bioZGA1k1/WSmd7x9T7WKfq15TpHW9uU2nUX+pHWhE4yQ+gqFlr1d7ermPHjikej2cVLpOJiOLJzEOgb3Bcjz17Rkc6epVIvh4us8UTgXoHxvWPD51QECx89LTSETAAilpnZ6e6u7uzCpYZk4mSrNobIz1/vE+J5MLrSgZW/YMT6uwp7iHPBAyAonX69GkdPXp0ye+3NrvzJMlk5kck8USgZ1+6kG1JKwoBA6AonT9/Xu3t7de0jBJ/8eHLs1kpq26v8/2XFh1ksJIRMACKzszUL0vpFputomRKLkcWGyMlEtdWYyEjYAAUnYGBAcXj8WtejjFSbWxcmYwojvieaipLs1u+pEikeHfDxfvJAKxa/f39GV2dn4nGqkmtqytTxJ8/ZSK+UXNjpQ7ub1JJhoFhJLVurC3qiy+5DgZA0clVuEQiEd12222qqq7Ro8+cUccr/TLGXO7WKol4stZqX1uDDh3YKGutHnn6dEbL9n1PB65fn5M6CxUBA6DoxGIxeZ53Tedgtm7dqtbWVpWVlUmS3vyGzbrz5mYd63xN/UMTkqSGteXatXWtoiUz18oY3XeoVf/y6CklFhhRFvE97dlep6aGyiXXtxIQMACKTlNTk44fP571+zzPU2VlpQ4ePKhoNHrV66XRiPbvWrfgMra21Ohtd23TNx/rlBQOR57h+0ay0g27GnTnzS1Z17fSEDAAik5ZWZnWrl2rgYGBjIYBG2MUi8W0bds2bdq06fLdLJeqtaVWv/LuG3Sia1AvnejT+GRcEd/T9k212rdznSrKsruAc6UiYAAUpf379+uJJ55QPB6fN2SMMaqurtYNN9ygysrKnJ5wj/ierttWp+u21eVsmSsNo8gAFKWysjLdeeedqqqqku9fOZ+Y53nyPE/Nzc264447VFVVVdSjuZYLRzAAilZZWZkOHTqkoaEhdXd3a2xsTJ7nae3atdq8ebNisdhyl1jUCBgARa+2tla1tbXLXcaqQxcZAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwIrLcBQCrnbVW53rH1H1+RNPxpCrLomrbskbVlaXLXRpwTQgYYBl1nR3Ww091a3wyoXgikCR5ntEPnjurpnWVessdWwgarFh0kQHL5FjngL7xyCkNj01fDhdJCgKrZGDVc3FUD/zLyxoanVrGKoGlI2CAZTA8NqUHn+xSImnnbWOtNBVP6usPnZC187cDChUBAyyD545eVJBBZlgrjY3Hda53zH1RQI4RMECeWWv10ol+BZkkjKR4ItBzx3odVwXkHgED5FkiESiRDBZvOMvg8KSjagB3CBggz4xnpCxPqXiecVMM4BABA+RZxPdUWRHNuL3nSc3rKh1WBLjBdTArWN/EmNoHz2kyGVd5JKp9a5u1prR8uctCBg7sadT3f3j2iuHJ8zHG6IZd6/JQFZBbBMwKdPbSkL586ll1j70mWSlpA/mep6++ckQ7axv1c9tvUX3M3TfeeDyunp4ejYyMSJKqq6vV0tKikpISZ+ssNtdtq9dTL55fNGB832hrc41qq2N5qgzIHZPN+PoDBw7Yw4cPOyxn+cxsB2MKu6/71ZF+ffql72kqSKR93cgo5kf0kRvu1frympyuOwgCdXR06MyZMzLGKJlMSpJ835e1Vi0tLbr++uvlebnteQ2CQFNT4cWG0WhUvu/ndPnLZWBoQl/59jFNx5NphyxHfE/r1pbpnffuVEmE3mwUJmPMEWvtgbSvreaAuRSf0g8uvqKHzx7X8PSEJGltaYV+tGW33rBuq2KRwvpGPpmM6yNP/5MmkvFF29ZGy/Qnt94v3+RmxxQEgZ5++mkNDQ1dDpa5PM9TbW2tDh48uOSQSSaT6u/vVzweVzKZ1OjoqHp6ei5/AbDWqqmpSa2traqurl7y5ykUY+PTeualC+o41a/wu42RtVaxqK8D16/Xvp0N8nMc2EAuETBpnBru1V90PKrAWk0HV+4wo56vEs/Xb+69Rxsr1yxThVd77NxJfe3VH15VbzqlfkTvb3uDbqjfmJN1nzx5UidPnlQQLNyl43meWltbtWvXrqyWH4/Hdfz48ctHR0EQzLsuY4w8z9OePXu0adOmrNZTqOKJQH2vjSueCFQei6h+TVnBH00D0sIBsyq/Gp0ZG9Rn2h/RZDKRdmc9HSR1KTGtP33xIfVNFM4V1A+dPZZRuEjSVDKh7549lpP1BkGgzs7ORcNlpm1XV1dGbWdMTU3p8ccfV3d3t5LJpBKJxILvt9YqmUyqvb1dFy9ezHg9hawk4qlpXaU2N1WrYW054YKisGoCxlqrV0b69LmXn9CfPPedjHbUk8mE/qnr+TxUl5nB6fGs2vdN5iYcBwYGspoLy1qrvr6+jNs+88wzmpyczHq+rZlzQszTBRSmVTGKbDwxrb9of1SnxwaVsJkdAUiSldULAz0ai0+psmT5p0zP9jttrr4DT0xMZB0wk5OZXXk+NDSksbGxJYfE1NSUhoaGtGZN4XRlAggV/RHMdDKhT73wXXWNDmQVLjN8z9PJ4cKYB6qxLLuT2i0VudnpZnvC3hiTcRdPZ2fnvIMGMhEEgQYGBpb8fgDuFOURTNfogB46e0ynRvo0Hp/SVIbnLdKxVvMOCc63e1t264FTz2gquXg9pV5E97bszsl6a2trsz6Cqa2tzajtzLU0S2Wtzep8D4D8KaqAmUzE9dmXH1fnaL/iQSCb7YRPaRhjVBHJfFoPl26q36h/ePU5TScTC34yzxjVxSrUVpObq78rKytVVVWl4eHhjNtnOoT4Ws+f+L6v0tLl774EcLWi6SKLB0n92UsP69RIn6aDZE7CRQp3gLtq1+dkWdcq4vn6rX1vVkWkVP48Z1h846k2Wqbf2Ht3Tkci7d69O6OuMs/ztHt35kdOVVVV11KWrLXasGHDNS0DgBtFEzA/uPCKzo8PK2Fz110SMZ7uWL9NJV7hXDm+rqxKH7/5Pr1h/TZFPV8xv0QxP6KYX6JSP6K7NuzQx268TzXRspyut76+Xnv37l0wZDzP0/XXX6+GhoaMl7t169YlX5lvjFFjY6Oi0cI4wgRwpRXTRWat1fHhi3qw56i6RgeUtFY10Zjubtqp2xq26MGeoxlfI5IJ3xjVlpbpJzbvzdkyc6UmWqaf33Gr3tV6o04N912e7HJHzTqnYbhx40ZVVVXp1KlT6u3tvRw2QRCooaFBO3bsyPjcy4y6ujrFYjGNj49n1V1mjFEsFtPevYX3/7NaJRIJ9fT06Ny5c4rH4yopKVFLS4uam5uLZnofZGdFXMk/Fp/SZ9of0YXx4bRX3UtSYG3Ojl48GW2sXKNf33OXqqJMMpjO9PS0xsfD63LKy8uv6ShiYmJCTzzxhOLx+KIhMzNCrba2VgcOHODopUB0d3ero6PjijnqJF0Olr1796qlpWW5yoNDC13Jv+xHMPEgqb6JMSVsUrXRMlXP6dqZTMT1xz/8loZSc4XNlcujFim8duTXr79L162hX38h0Wg0Zzv3srIyHTp0SB0dHbp48aKMMVfMPRaLxVRWVibP81RVVaXNmzersrIw7o+SSCQ0MRH+bpaVlSkSWfY/qbx79dVXdezYsbSj+WbC5sUXX5S1Vhs35mbqIqwMef9riAdJ/bD/tI4PXVDX6KAuTozIN56MMUoESW2uqtNbN16nvWublQwC/bfn/23ecMk1I+lAw2bCZRnEYjHdfPPNmp6eVm9vr6anpxWJRFRfX6/y8sK7x83IyIhOnTqlCxcuXB5MYa3V+vXrtX379qKYiDMTExMTOnr06KJDxYMg0EsvvcQ5s1UmbwFjrdW3z3ToOz0vK7BW8VlHHrO7tl4Z6dPfHP2+3tDYqsn4tM5PXNt1Etl6745b87o+XCkajRZ8V8rZs2f1wgsvpN2pnj9/XhcuXND+/fvV3Ny8DNXlV3d3d1btT58+re3btzuqBoUmLwFjrdUXTz6tw33dGXVpTQdJPXmx84oQyocSE47KAuYzMDAwb7hI4e+6tVYvvPCCYrGY6urq8lxhfp05cybjC12DINCZM2cImFUkL8OUD/d1ZxwuM/IdLpJUEeWCPSzs5ZdfznhW6aNHj+ahouWVSGQ3y8X09LSjSlCI8hIw3zrTkfOT8fOpjERV6md/YFbieXpj4zYHFaFYjI2NaXR0NOP2IyMjGhsrnNs9uJDtPHUMV15dnAfMhfGRnE0bvxjfePrV696kD+66QzuqGxQ12fwyG925gUN3zG9wcDCr2RGMMRoaGnJY0fKrr6/Pqn02F+Fi5XN+Dua1qUuKGE9xuT+CKfNL1FpdL2OM9q4NT7A+eaFTf/fKswseQUU9X+9qvSnnV7+juARBkPXcadcyU/RKsG3bNvX29mb0OWfudorVw/kRTK7uCZ+Ju5rarvqGefv6Vv38jltV6kVU6l2Zp6VeRFHP189uu0WHNuzIW51YmWKxWNZHMLFYcV+oW1tbq/r6+kW7yjzP04YNG6557jmsLM6PYJoravJywt6T0Zubd6Z97dZ1W3VD3UYd7j+tpy6+qkuJKZVHojq4bqtuadis6BLO2WD1aWhoyHoC0dXQJXTTTTfpyJEjGhgYSHsk4/u+1q1bp/379y9DdVhOzveslSUx7V3brOcHzmQ1v7FnjCr8qEYTUxm1v3XdFpUtMK1+1I/o9sZW3d7IITqWxvM8bdmyRZ2dnYuOJJtpm+1J8JXI933dcsst6u/v16lTpzQwMHA5iOvr67Vt2zbV1dXldHZvrAx5+er+9s171TF4LuORZJ6MKiOl+u19b9aft39Pr03Nfy96T0bV0Zje1XpTrsoF5tXW1qaBgQENDw/PGzKe56mmpkZtbW15rm75GGPU0NCghoYGWWuVTCbl+z6hssrl5etVc0Wtfmn3GxX1fHkL3CneSCrxfG2vadDHbnqrGsur9bEb36rt1Q2KmCvfaRSenG+prNXv3fhjqizhGha453meDh48qKamJnmed8URyszPTU1NOnjw4Ko4eknHGKNIJEK4IL+zKZ+7NKxvn2nXD/vPKOL5krWaDpKqLCnVhvJqbatu0O2NrWoou/pEYM+lQT189rjOjA3Kyqq5vFZ3N+/UlqrivlIahWtqako9PT2Xb/tcXV2tjRs3MtcWVpWFZlNelun6JxLTujgxqqQNtLa0QmtKC28yQwDA4gpuuv6ySJQjDwAocquzkxgA4BwBAwBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYA4AQBAwBwgoABADhBwAAAnMhqNmVjTJ+kbnflAABWmM3W2rT3Bs8qYAAAyBRdZAAAJwgYAIATBAwAwAkCZgUzxrzPGGPneQwtc21fMMb05HiZvjHmN40x7caYS8aY88aYrxtj9qVpu8cY86AxZswYM2CM+bwxZu0S17sltU0/cO2fIr+Wuh1mfeZ0j9o5bTcZY/63Mea0MWbCGHPCGPMJY0zFnHaPzrO835jT7u3GmC+nlnMHldQAAAhMSURBVBMYYx7NycZA3i3LLZORc++SNHdnnliOQhz7Y0m/K+mTkr4nqV7SRyU9YozZb63tkSRjTJOkRyUdk/RTkmol/Q9J/2qMeaO1NliG2vMuR9vhk5K+Mee50VnrqJD0kKQSSb8v6bSkWyT9oaQdkn5mzntflPTLc57rmvPzv5N0g6SnJMUyqBEFioApDs9ba08tdxF58D5JX7HWfmzmCWPMi5KOSvpxSX+devp3FO7w3m6tHUq1OyfpMYU7r3/MY83LKRfbodNa+9QCr9+hMEjeYq19MPXcI6mjpN82xpRba8dntR9dZHmS9MGZ8DPGfD+DGlGg6CJbBWZ1pR0yxvzTrO6SvzTGlM1pu8EY80VjTL8xZsoY86Ix5r1plrnVGPMlY8yFVLtOY8xn0rS70RjzhDFm3Bhz0hjzoWv4KFFJI3Oem+kKnP27/BOSvjmzU5Uka+3jCr9d338N61+QMeZWY8xDqe17yRjzsDHm1jltbjHGfDe1/SdS2+2vZr2+PtXddC61Xc8bY/7VGLNuCSXlYztEU/+m+3/xJJlsF7hajjBXAwKmOPjGmMicR7r/2wcknZL0DkmflvRBSZ+deTHV3fGYpLdK+j2F33JfkvQlY8wvzWq3VdIzkg5J+rikH1PYJVI/Z33Vkr6cWu/9kp6V9FljzI/MbpQKvy9k8Dn/StJ7jTH3G2OqjTGtqed6JH01tawySVsltad5f4ek6zJYT9ZS54Eek7RG4ZHWLyj8/I8ZY/an2lRK+jdJyVSbt0r6I13Zk/AlSW9QePTxo5I+rPDzlc9a16PGmK5F6snVdvikMSZhjBk2xnzDGLN3zusPSTop6b8bY64zxlQaY+6W9J8kfc5ae2lO+xtTy4qnvrz8YoZ1YCWy1vJYoQ+FOyk7z+Nf07T73Jz3f1Thzq4t9fOvpdrdNafdQ5J6Jfmpn78oaUxS0wK1fSG1rB+Z9VyppAFJfzOnbULS/8rwM38sVfPM5zwuadus15tSz38ozXsfkPTKErbzltQyP7BAm68p/NZeO+u5akmvSfrH1M8HUsvZt8ByxiR9eJF6HpZ0apE217QdJG2Q9DmFX0buVPhl5FWF5192z2m7TtITc37//laSN6fdH6WW8yaFXzj+IdX2YwvU8X1Jj+b7b4tHbh6cgykOP6mrT/KnG0X21Tk//72kT0i6VdIJhUckZ621j85p94Ckzyv81vuSpHsVBti5Reoat9Y+MvODtXbKGHNC0qbZjay1Gf0eGmN+RWEofkLSIwqPmD4i6UFjzJ0Z1OPSIYXbZHZ31Igx5huS3p566qTC/5e/Nsb8paTHrLVn5iznWUm/Y4wxCgcytNvUnnbWcu9x9SFmreO8pNndmU8YY76j8Ojno5LeK0nGmJikrygMmZ9X2P12q8Ij24SkX5m1zI/PWc0/G2O+Lumjxpg/t9aOOfo4WCZ0kRWHdmvt4TmPdCf9L87zc3Pq37WSzqd534VZr0tSna4OtHQG0zw3pSWMDEqdNP60pE9Za/+LtfZRa+3XFIZdg8IuJSncgVuFXVVzrVV4ROHCQttujSRZa4cl/Yikcwq79k6bcMj1O2e1/xmFo7b+s8IRV2eNMR+fp8tzITnfDqkw/L7CUWIzflHSXZLus9Y+YK193Fr7KUm/JelDM92DC/g7hb8Pc7veUAQImNWlcZ6fz6b+fU3S+jTvWz/rdUnq1+uhlC9tCrvYnp39pLX2NUmvSNqd+nlc4bDXPWmWcZ2klx3Vt9C2uxy01trnrbXvVLiTf4PC2r9qjLk+9XqvtfY/WmubJe1S2NX4h7p6aO+CHG+H2UdUeyUNWmtfmdPmmdS/u5ewTBQJAmZ1+ek5P79bUiDp6dTPj0lqMcbcMafdzyk8BzOzU3pQ0tuMMRtcFZrGzFHU3FFZayVt1+shKYVHAD9ujKmZ1e6Nkjbr6ms6cuUxSfcZY6pmrbNKYffYo3MbW2sTNhyu+/sK/w6v2hFba49ba39PYUBdv4SacrodjDGbJL1Rr4eHlDpCM8Zsn9P8ttS/Z7Ww90iaUNj1imKz3CeBeCz9oddP3v+UpINpHpE57U4rvNDuRxX2o09L+vys5VUoPBdzUdIHFI4O+1Lqvb80q90WSX0KT/p+UGG3z3slPTCrzRck9aSp+VHNOWmrDE/yS/oXhV1sfyTpHoWBeTj1OQ7Mates8CjrsdRn+BmFt5l4SrNOPOv1k/d/sMh6Z9p9NrWt5z6ikvYp3FE+I+mdCk+OP516bn9qOW9TuGN/f2qbvU1hWI9IapFUo/AI7TdSdd8j6X+m1n3/rHoWPcmf5XZ4U+r/4BdmPfenCrskfzpV64dS7x2StHPOthlJ/d78+1Tb30k9d3hmPQoHCnxTYZfaPant88+pz/a7c+rePGvbHlV43mfm583L/XfHI/PHshfA4xr+8xYeRWYl1c9pdyj1Rz2msEvnLyWVzVnmBoWh0q9wZ/6ipPemWfc2hf3n/ZImFXb1/Nms17+gzAPGSvpCBp+3XOE3/pclXVJ4zuObkm5N03avpO+m2g2m6qmb02aP5hlpNafdlgy3820KR9yNpdb78OzaJO1UeEL81dQ265P0LUm3pV4vVXixaEdqGSMKA+fn0mzDrgx/RzLZDnelPsf7Zj33/tS6ByXFFR6pfFmzwmVW2+sUDiA5ozBQT0j6lKQ1s9psl/RthUc0U6nP96Skn83y9/p9mXxuHoXx4H4wq4Ax5n0KR4HtsKvjiv+MpK7t+a8KvxWPL9YeQHY4B4PV7E2SPk24AG5wHQxWLWvte5a7BqCY0UUGAHCCLjIAgBMEDADACQIGAOAEAQMAcIKAAQA48f8BsJR3LsKrHIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GaNeuXVdV85hEwADAgpbTKW+M0a233nrVHb1InGgJAAvasmWLLly4kPMFKqurq7V3796r4pyX2VydnxoActDQ0KDXX3993oCxVrqUTOjCeJWSQUwyRtXjcVW/2a+bdm5Qovzq29zSRAYAC/A8T3fccYc8b/ZNZirw9dbFOvWMrNFkEJeViQJnLKUXjpzTX37pNZ05P7zCVRceAQMAi1BXV6e77rpLsVhMvu9PPx+ERmeGapUOPdlZNqnpIFQqHeqfvnNS5/tGVrLkgiNgAGCR6urq9O53v1u33nqr1q9fr5qaGo2G6xXKlzT/+S3pINS3XuyQJIWhVVfPJZ04Paj2zosaG0+tQPUr7+prFASAZfA8T42NjWpsbFQ6CPXnnz8iaxd3jsyFoQl99+AZHT89qFQQTkdSEFg1b16j+29v0oZ1Fe6KX2EEDAAsUU//aE7TB6HV94/3KpzlFjLtnUM6c/6S3veOHdqyaU2eKiwsmsgAYImS6dzP7p8tXKak0qG+/MxJDY9MLqOq4kHAAMASJcr8hSfKURhavXqsN+/vWwgEDAAsUf36Knlefi9eGYRWr7f1KVjmtc+KAQEDAEvkeUa376qXn+eQCUOrsfHlXf+sGBAwALAMe3bX65qa8vyGzCq5pD8BAwDLEI/7euLRG7SxtlLxmHfF2TDxmKeYb3LLDGtVkSj9Qb6l/wkAoMAS5TE98dgN6u4f1aE3etQzMCobWl1TU647dterdk1Cn/1Kq4JgniFkGcZI17esV8wv/f1/AgYA8sAYo0111frhB6tnfX3zxmp19YwomG+csiTf87TnxnoXJa640o9IACgBP/TAdlVXxecddRbzjd5131atX7s6zuYnYABgBVSUx/SB9+7WDS218n0T9deYaCRazPdUe01C73vHDu3avr7QpeYNTWQAsELKy2J6ZG+LHrr7Wp08c1Gj4ynFfE+b66tVv76q0OXlHQEDACusvCymG3dsKHQZztFEBgBwgoABADhBwAAAnCBgAABOEDAAACcIGACAEwQMAMAJAgYoYdZaWbvwBRSBQuBES6DETCYDtbb363Brjy6NJiVJ1ZVlunN3vXbvWK9EGX/WKA58E4ES0jMwqi89fUJhaJVKv31L3UujSe1/pUsvHjmnH33XTm2qm/2KvsBKookMKBEXhib0hW8c12QyuCxcpqSDUJOpQF96+oQGLo4XoELgcgQMUCKeO9Q5a7BkS6VDPfdy5wpUBMyPgAFKwOh4Sh3nhhc9fWf3pen+GaBQCJgS1TM2rOMXe9Q+3KfxdKrQ5cCxzu5L896oKpvnGXV2X3JYEbAwOvlLiLVWL/d16Gudb2hgYkS+8WQlBTbUng1b9NiWm7SxoqbQZV7GWquBgQGdOnVKIyMjkqQ1a9aopaVFtbW1MmbxG82rWSoV5DQc2VqrVCpwWBGwsKsmYEJrNRFEe/oJPy6vxDZsobX66+Mv6MhAl5JhOvPs2xuQA71v6ZX+Tv3STQ9qxzUbC1NklrGxMR08eFATExMKguCy5/v6+lRZWal77rlHiUQi78sOgkBBECgWi8nzLj9QHxsb01tvvaVz584pCAL5vq9NmzappaVFVVXFedOnRHks851dXMgYzyhRftX8eaNIrfpv4IXJMT177oSeO9+mZBht5Mo8Xw9s2qkHG6/TuvLKAle4OF8+/X0dGTg7/RmyWUmTYVp/+saz+s07fkjrE4XdUE5MTGj//v1KpVKz7nkHQaCRkRHt379f+/btU1lZ2bKXaa1Vd3e32tvbdfHiRXmeJ2utNmzYoO3bt6u2tlbHjx/XW2+9ddkJiul0Wh0dHTpz5oyam5u1a9euojuy2tq4RmEORzBhaNW8eY3DioCFreqAOXahW59s/Z5CGypt3x59Mx6E+nbXm/ruuRN6cvc+7VrXUMAqFzaRTuk7544rNUe4zJQOA3377DH95I49K1DZ3FpbW+cMlynWWk1OTurNN9/ULbfcsqzlpdNpHTx4UMPDw9NHS2EY/c77+vo0ODioRCKhiYmJ6eeza7HWqqOjQ5K0e/fuZdWTb2VxX7u2rdcb7QMKw/mDxjPS9c21KueESxTYqu3kP9x3Rn969Bklw/Rl4TIlbUNNhml9svU5nRkZLECFi3ew9/Sif1GBtXq+59SiwsiVZDKp7u7uRfUZWGvV1dWldDq94LTzvcdLL72koaGhy5riZgqCQKOjo3O+PnO606dPT/cXFZO9dzapMhGbt3nXGKkiEde+PU0rWBkwu1UXMOkw0F+9+bz+4s39WviMASkZBvqHU686r2s52i/1aTKXwDDS4MSou4IW0Nvbm1MTkzFG/f39S17ewMCAhoaGZj0yWQprrU6fPp2X98qnivKYfvq9u7V+bULx2JV/uvGYp9prEvrp9+5SZSJegAqBy62KY+hUGOjV/k6dvjSg1wa71D+R297nyeE+DU6MqrbA/RZzSee44TSKRpYVykJNY9miEU9LH2rd3t6+4JFJLqy1On/+vG666aa8vWe+VFXE9YEf3q3zfaM63Nqj/gtjkqT1ayt0x+56bd5YXXT9R7h6lXTAhNbqa2eO6umuY5KkyWBpzSy+5+nEUK/uTbTks7y8aahYI994iw6NdBjqmrLCDV6IxWI5b+R831/y8gYH89/EuZwmO9eMMWrcWK3GjVxvDMWtZJvIbGbY7jfPtmoySC85XKbea67RWcXgBxq2KYdz7HTD2gZVxZc/KmupNmzYkPMRzIYNG5a8vHw1jc0Ui5X0vhdQFErir2hqqPHxiz1K21B1iWptSFTPO2w3F54xWlOW/3Mx8mVDolo712zU8aEeBQtsuMs8X49cW9gRUBUVFaqtrV10v0p9ff2yhinHYrFlNbFlM8Zo06ZNeXs/FI8wDNXT06OLFy8qDENVVVWpsbExL8PkcaWiDZjAhjrS36l/7nhdPeOXZCSFmZPMOkcv5HVZYWi1e21xD1X+uRt+QP/t1W9oKDkxZ1NZmefr0Wtv1M4iONHyxhtv1P79+xfsG4nFYrrhhhuWtaympiadPn06bzfeMsaopaU4m0uxNFMDN06cOKEwDKe/l57nqbW1VZs2bdLNN9/MkWuerfjanOqQf/Nij86MDKhvYkSBtSrzfN20rlHvatqljpEBfenUq5oIUtPnLbu8Z9/a8kqV+cX9xaqOJ/Rrtz+qv2k7qKOD5yQZpW30R1LuxRT3ff1o8226v2F7YQvNqKmp0b333quDBw/KWntF0Pi+L8/zdN999y377Pnm5mZ1dHTkJWB839fWrVuL9ox+V4aGhtTe3q7BwUGFYajy8nI1Nzdr8+bNq2Kj29raqjNnzlzxPZxqXj1//ryGhoa0d+/eVfF5i4XJ5Y9yz5499tChQ0takLVW3zjbqm90vqFUECiYJTKMjDwTdd6v5E1gY8bT79/zuKrj5Su41KUbTo7rpb4ODU6MKu772rGmTjeu2yTPFF+XWiqV0tmzZ3Xq1ClNTExIiprQtm/fnteNV3t7u06cODHvEZMxRvX19ert7b3iVsPGGBljivZMfleCINChQ4emg2XmOpkaeLFnzx7V1dUVqsRl6+np0SuvvLLg0bTneWpoaNAdd9yxQpWtDsaYw9baWc/sXpGottbqs20H9XJfx7x9JlZWQQFuL26M0ct9HXqo8bqVX/gSrCmr0Ds3L69ZaaXE43G1tLQ4b3Lavn27fN/XsWPRiMKZGxPf92WM0e233676+nqNjY3p9OnTOnfunNLptHzfV2Njo5qbm6+qI5cwDHXgwIE5zyGaWocvv/yy7r777mUNxCiktra2RQ1jD8NQ3d3dSiaT9MnkyYoEzKG+jgXDpZBSYZDzuTMoPs3NzWpqalJXV9d0eJSVlenaa69VQ0PD9EUvKysrtXv37qK7HMxKO3funIaHhxcchReGoY4cOaKHH3645I7sxsbGNDy8+PvoSFJXVxd9cHmyIgHz1c43ijZcpsS94mteQu5isZi2bt2qrVu3FrqUonfy5MlFn6CaSqU0MDBQckcx4+Pj8jxv0UPZwzDU6GjhroKx2jjfqnaPDRX90UG5H1NLTWn94QDLMTExobGxsUVPHwSBurq6HFbkxlKOuLJv74Clc74mByfHFCvCzueZYsbTzbWNhS4DWDGpVCrnjW8yWXq3YK6urs7pRFzf97V27VqHFV1dnG/5/RUMFyOpwo8r7nnaUrVuUcFW5vl6/9Zbi3IEFuBKLBbLeVh3PF56F9AsKytTfX39oqc3xqihobjPiSslzvtgGiuvWZFLx8fk6b76bbqnvlkbEtVaV16p1gvn9anW7ykdhtMnac5U5vl6R+P12te403l9QDFJJBJKJBKLbiabuutnKbruuuvU29u7YH+T7/vauXMnTWR55HxN1pQldGNto5yPPTHS+5pv0c5rNk7fpXL3uk362O2P6p6NzYp7vhJ+XBV+XDHjaeeajXpy9z493nKb68qAomOMmR7avRi+72vjxsJfIWIpampqdOedd877WX3fV1NTk7Zt27aCla1+KzKK7Ie33KxjF847G0lW5vl6d9Mu1cxyPbGGyjX60PX36Ynte9QzfkmhDbWuvFJrS+RWyYArTU1N6ujo0MjIyLz9FJ7n6dZbby25Icozbdy4Uffff7/a2trU09MzfZQShqFqamq0Y8eOkj1CK2Yrdib/awNd+ss39ysVhprrPH0jKeb5umP9tSr3fR3oPS0jo1QYzNrE5Rkj33h6R+N1erz5tpL+AwAKIZVK6eDBg7p06dIVTUhTG+HbbrtNjY2rZxBMMpmcDtWKioqr6uRaF+Y7k3/FAkaSukYv6mudR/X9gbPy5SltQ6VtKE9GlbEy7anbqoc3X6+NFTWSpGSQ1vmxYaXCtAYmR3Wg97SOX+xRYEOVezHdWbdF79x8gzZXMeoDWCprrQYGBtTe3q4LFy7IWquysjJt2bJFW7ZsUXl5aVxCCYVRNAEzZTSVVM/4sEIbqjZRpdry3PYgrLUcrQBAESj4tciyVcXLtC2+9BMbCRcAKH6MxwMAOEHAAACcIGAAAE4QMAAAJwgYAIATBAwAwAkCBgDgBAEDAHCCgAEAOEHAAACcIGAAAE4QMAAAJ3K6mrIxpk9Sh7tyAAAlZqu1tm62F3IKGAAAFosmMgCAEwQMAMAJAgYA4AQBU8KMMR8yxtg5HhcLXNtTxpizeX5P3xjz68aYt4wxk8aYNmPMR+aYdq8x5gVjzLgxptsY8wljTMUSl/tgZp2+c3mfYOUtdT3M+Mzzfq+MMQ8bY/7WGNOeWUa7MeZTxpiNWdP91jzf1Yl56ngiM01ev0tYGQW5ZTLy7sclZf8BpgtRiGOflPQhSb8t6aCkhyT9oTGm2lr7O1MTGWNukfQtSd+U9F5JLZL+QNJmST+5wjUXTJ7Wwy9JennG/7O/V78gqVrS70g6JWmnpI9Leo8x5hZr7Uhmuv8t6RtZ81ZlnvvnOepfK+mPJXUvslYUGQJmdThirT1Z6CJcMsZskfSvJP32jDD5ljFmjaSPGWM+aa0dzDz/cUWB++PW2lRm/qSkzxhjft9a+8pK118g+VgPx6y1B+Z5/d9aa/tm/P85Y8wJSc9J+glJn5Yka+1ZZe0EGWN+RtE26DNzvPf/kPR9SeclldzRI2giuyrMaErbZ4z5J2PMiDFmwBjzZ9nNJcaYTcaYvzHG9GeaoV4zxnxglvdsMcZ8NtPsMmmMOWWM+ZNZprvdGPP/jDFjmSatX1jix7hb0ff161nPf0NSQtKjmeXFJT0i6QtTG9WML0hKSnrfEpe/IGPMI8aYFzNNRUOZdX191jTvyTRZDWV+D8eNMb8x4/XrjDH/1xjTa4yZMMacMcZ80RiT087gSq2HrHCZMnXEs3mB2X9WUo+iI6zLGGPul/QBSf9uWQWioAiY1cE3xsSyHrP9bv9W0klJPyrpf0r615I+NfWiMaZK0Z7no5L+i6T3S3pd0meNMf9mxnQtkl6StE/SbyjakH1c0oas5a2R9HeZ5b5P0YbnU8aYh2ZOlAm/pxb4jEHmZzLr+cnMz5syP7crCpyjMyey1k5Iape0e4HlLIkx5hFJX5U0oqj56clMTfuNMZsz02xT1Bz0VmaaH5H0CUVNRVO+qmjD/KSk90j6z4o+4zHXoRIAAAYySURBVPTv0xhz2hjz7AIl5Ws9fM4YE2R2SP4ucyS5kAcyP4/NNYEx5lpFTZyfs9ams16LS/oLSX+w2o/MVzuayFaHN2d57quK2t1n+pq19j9m/v20McZK+q/GmN+11p6Q9GFFbegPWWufzUz3dWNMvaTfMcb8lbU2UBQmFZJutdaem/H+2U0dNYqaUL4rScaY7ynaaP6UpO/OmC7Q2wEyl+OZn/dKenXG8/dlftZm/bwwy3sMzng936b6IB6d2mAaY16UdELSr0j6ZUl3SCqT9KS1djgz3zNTb2CM2SBph6T3WWtn9kv8Xday0lp4fS13PQxJ+iNFOxzDkm5XtNPxojHmdmtt72wzGWNqFPWbHJP0T/O8/wcUheZszWO/Kqlc0u8tUCOKHAGzOjyuKzv5ZxtF9oWs//+9og3j3Yo2hPskdc0Ilyl/K+mvFe31vi7p3ZK+khUusxmbChdJstZOZtrnL9sLttYu+D201rYaY74t6ePGmFN6u5N/ahRZuNB7uJI58rtD0u/O3Bu31r5ljHleb+/RH5GUkvT3xphPS/pe1oZ6QFFI/fdMqD9rrW3LXp61doejjzJzGa/q8iB/LrOD8JKijv9fy54n04z3fxQdgd2ffWSS5YOSXrXWvpb1HjskfUzS45mjLZQwmshWh6PW2kNZj9maFnrm+P9UW3mtog7VbN0zXpek9boy0GYz297zpKKmm6X4kKRWRf0uFyQ9Jemjmdem6p5a5rpZ5q9VtPeeb+skGc297molKfM7eY+iv7vPSuo2xhwwxjyQed1KepekQ4r23k9k+raeXEJNeV8PmUEBJyTdlf1apkn2M4o649+fHRxZ094t6QbNfvTyp4qO6g4YY9ZmRpKVRbOZtdl9hihuBMzVpX6O/3dlfg5KaphlvoYZr0tSvxbuwM07a22XtfbBzLJvztR1JPPy/szPdkUhduPMeY0xCUnbFAVUvl2QZDX3upvemFtrv2utfUTSWkUb47Skr2aax2StPWWt/aCkOkXNUs9I+qQx5tEca3K5Hma7gOGfK+pXesJa+50F5v9ZRUdy2U1/UnSU/JiidTr1+ClJjZl/02xWQgiYq8tPZP3/CUVNSwcz/39OUlNmBM9M/0JSr97eKD0t6b3GmE2uCp2PtfactfaopAlFTWRvSno281pS0RHOT2SNvPoxRe36s55zscx6RiUdlvTjxhh/6nljzFZJPzBVW9Y8k9baZxQNxa1SdI7KzNettfaIor4b6e1BDIutKe/rwRizR9L1iprJZj7/R4qGkH/YWjtfv4uMMWWKvndfn2ME2hOKmj5nPr6paKfmIUn/K9e6UTj0wawOt03tAWc5lNUO/pgx5g8UBcTdkn5T0t/MaOd/StK/l/SPxpiPKWoG+2lFzTY/n+ngV2a+xyS9YIz5XUUj0zZLesRae8WQ5oUYY9KSPmOt/bkFpntSUai8pejI4Gcl7ZX0sLV2Zh/Mb0k6IOkLxpg/k9Ss6ATDL1lrD894vwcVDTb4sLX2qUWU+oOZJpuZ0pmN6q8rGljxFWPMJxWdfPhxvd1ZrswQ7X2SviapU9Gou49KOifpqIlOjPwTSZ9XtE59Rc2CaV0+GOCkpA5r7cML1LvY9fBBReerPGytfS7z3OcUredXFPXn3Z6ptUtRM9bUvL+qKAQ/LanNGHPvjOX3WWvbs2p6r6ImulnPfZntnBtjzIckTc7SN4hiZ63lUaIPRRsfO89jQ9Z0+yR9WdFQ2kFJfyapIus9NynqH+hX1MTymqQPzLLs7Yo6dPsVbfTbJX1ixutPSTo7y3zPKuq8nvmclfTUIj7vLyoaTTaRqf8fJd04x7T7JL2YmbZH0cimyqxpfiiz7EcWWO6D86zjkRnTPZJZ5riiYPmypOtnvH5f5rnOzLo9L+mLU9NI2qhow3tC0ljmMz4n6T1Z9ZzOXofz1L6Y9TD1/XhwxnMfzfzuhxQ1Z3UqGjq8aZbf51zr5orfaebzD0gqy+F7Put3iUfxP7gfzFUgswf415J2Ws4rmJY5+voRSTdb/hCAvKMPBlezBxQNLSZcAAfog8FVy1qbPZgBQB7RRAYAcIImMgCAEwQMAMAJAgYA4AQBAwBwgoABADjx/wE8XVGaJV6jVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AKjOeyenl7su6MpxRGJja25Zoy4aWac91qWYEDABUiexEpH87cFrHTw/KZMpFyeX706lAYWB64z3rdPfO1RU/I3+uCBgAqALZiUh//cWXdGU4oyhyKrwQ/0Qu1oSkxw+e1dC1rB64d+OC1VkKxmAAoAp8Zf/pgnCZXi6K9dzxfnWduVLByuaOgAGABTY2ntMrPYMzhsukXC7WFx7r0ktdA9e70KoVAQMAC+xb3ZdLap+LYn1l/2n92d8/r/7Lo56qmj8CBgAW2ODQWFF7L4UmcrHGxnP623/5lgaHxj1VNj8EDAAssDCY+6o4OxHrq0/1lLGa8iFgAGCBtbctUV0RNw67ld4L13RtJFvGisqDgAGABbZtU0tyd7A5MjN1n71avoLKhIABgAUWhoHecOdapcK5rZLjOFYmG5W5qvkjYACgCnTubteura1KzaGrLAgC1df5v7lYqQgYAKgCZqbv3nubHtq3WW0rGkp6r5PT5vXLPVU2d1wqBsCMxsYndPbisCZysRrrU9rQvnTOXTmYmZlpx20rtOO2FfrCYyf1yulBzXZXe5O0Yc1SLV1SV5EaS0HAAJjW4NC4Hj/Uq+7eqwpDk3PS5DUW79yxSnvvWleRe76/Wn3X6zep98I1jWVyM4ZMOh3qu96wqXKFlaAmA8Y5p6GJcU3EkZak6tWYSi90SfPWM3xZX+n9lo4M9CoT55QOQu1esVZvXn+Hti5rq5mrp2JxuHBpRJ/50svK5WI5SVF84xru8EsX1XXmqn7oHberoa4mVyNVr6kxrfe/4w595ksva3Q8p4ncjZeFSacCpVOBfuAtO7RiWWldapVibrb9rwKdnZ3u4MGDHsuZ2XhuQt/sO6kv976k4YmMQguUc7F2LF+lt27Yrdtb1tTcijh2Tn9z8qD293UpF8eKC66gapLSQUp7WtfqAzvvUypga1FKNjD6+/t18uRJDQ4OyjmndDqtjRs3qqOjQ42NjRWrJZfLKYoipdNpBfM4Wa5QNptVb2+v+vr6FEWRGhsbtWnTJrW1VWZDYyyT059/9uisRyWFgWntqma996Gd3mt6NYtjp+6zV3Xw2AUNXB2Tc9LypfXq3N2u7ZtaFC5wd6WZHXLOdU77Wq0EzOXxEf2357+skYmMsvHNv/h1Qag3rN6sH952b02FzN+dfFaPX3hl2s80KR2EurN1vX7yjn0VrKw6ZbNZPfXUUxoeHlYU3bjMJlfwe/bs0aZN/roMoijS2bNndeLECY2NjcnM5JzT6tWrtXXrVrW2ts5pus45vfzyy+rq6pKUHHo6KQxDpdNpdXZ2qqWlpSyf41aeOXZB+4+cVa6IS5ekQtP737FLbSsqF+qoLjMFTE2M1I1HE/rI81/WlczoLVfE2TjS/gsn9Y+nn6twdXN3aXxYj50/PmO4SNJEHOno5bM6dW2gQpVVpyiKdODAAQ0NDd0ULlKyQo7jWMeOHdPZs2e91DA+Pq7HHntML7zwgkZHR+WcUxzHcs6pr69PTz31lF544QWVsuE26dixY+ru7r7+OQpFUaTx8XHt379fV674vVT7sy/2FRUuUtJ19uxLfV7rQe2qiYB57NwrGsyMarZf+Zyc/vXMixqZqL5LJkzn6+eOz/qZJk3Esb589iWv9VS7c+fOaWRkZNaVdxzHOnr06E0r6fmKokhPPvmkRkdHpw24yTY9PT165ZVXSpr2wMCAent7bzndwukfPHhwTgFWjCiONTo2UXR756SLA9V7NV8srKoOmFwc6S+OH9BnTx0pekUcS/qH7sM6MzyosyNXNB4V/8dSaYcHehW54laCTk4vXD7vuaLqduLEiVlXwJOcc7pw4UJZ53/27FllMplZ20VRpBMnTmhiovjfvZMnTxb92SYmJjQw4Gdvdi655SvsUPuq4vCPq9kxXZsYV32QUmt9k7qvXdZXz72sY5fPKRPnSp7e430n9cylHklOkXO6d9VteuuGXWpvWlb+4uchG5X22SZm6UpbzLLZrEZHi99SjqJI58+f17p168pWQykBZ2Y6c+aMtmzZMmvbKIrU399fdB2Te0ltbW1Fv6dYqTBQfX2o8Uyxn1Na2cL4SyVEUaSBgQHlcjml02m1trYqDKv7wJ+KB0wujnSwv0fP9J/WqWsDGouyipxT2gI55xRJCsyK3rK/lcI9lwN93TrYf1ofvON+7Wkt3wpnvppSdRqaKP4+DvVhVWwPLIhcLqcgCIpewUsqaQ+imPmPjY0V3T6KIl28eLGogJmYmLh+oECxxsf93f/j7p2r9cwLF4q6P0kqDPTaXWu81VJLnHO6cuWKzp49q/HxcaVSKa1Zs0Zr1qyZ1xGGmUxGx48fV29v7/Xfk8l/N27cqB07dqiurvpOspQqGDDOOX2p9yV9/vTzyk0THhMFz0Vl3uWO5ZSNI338pcf1y3e9RRubV5R1+nP1xjVb9IWeo0XtmYRmev2qDv9FVal0Ol3ymEo5/+iiKCo5BIoNwyAISu5m8rnletftq3XoxT5Fs3RMB2ZqWVav9rYl3mqpFUNDQzp06JDGx8dv+N7Pnz+vIAi0e/dubdiwoeTpjo6O6oknnlA2m532d+T06dM6f/689u3bV9HD84tVkTEY55w+deJpfe7Uc9OGS6Vk40ifO/38gs1/qn3tW4tuG1ig71r/6j3fIJ1Oa/ny4q+1FIbhnP6gZ5p/qSHQ0FDcyW/pdLqklUMYhlqzxt9ew5LGtN714LYZLwcTmNTYkNL3f/d2b3XUiqtXr+qJJ57QyMjITRsVURRpYmJCzz//vE6dOlXSdOM41v79+5XJZG75u+ecUzab1YEDB6pyLKwiAXPwUo+e6uuedYuoEl4cPK+hbPFdHT41p+v1g1teq7pZTqCsC0K9dcMdWt24tEKVVaetW7cWveWeSqW0atWqss07CAKtXr266PZhGBZ9Lo6ZacuWLUV/NudcWcNzOretW6b3PrRTa1ctURiaUqEpCEzpVKAwNO3oaNV/eNcuNTdVZ9dMpTjn9PTTT8+6txrHsV588UUNDw8XPe2+vj5ls7MfEeuc0/j4eEnjeJVSkS6yR3uO3dAFtpDSQaie4UHtaa2O3cn7126XyfQ3XYdk0g3nxKQtlJPT2zbu1ts27l64IqtEe3u7Vq5cqUuXLs3YXRYEge65556yn3C7detWXbp0qaiur3Q6XdIg/MaNG9XV1aWxsbEZt0TDMNS2bduUSvn/021vW6L3vf0ODQ6Nq+f8kHK5WI0NKW3Z0KKG+lfveGChixcvKpcr7mAd55y6u7v1mte8pqj2XV1dRXezRlGkkydPlrQRVAnef0sujF7VpfHiU9s3J837AIJy27d2m167apOe7OvSgb4ujeYm1BCm1LnqNu1r36ZlddV5naFKMzN1dnbq8OHD6uvrk3PuhpVxGIbX2/g4wqq1tVWbNm1ST0/PjH/4YRiqs7OzpIALw1D33XefnnzySWUymWmnH4ahOjo6tG3btjnVP1crljVU7bWuFtrp06dLOnS+t7e36IC5du1aSbWU2r4SvAfMQGZEoQWSquMQW+ecVtQ3LXQZN2lK1el71t+u71l/+0KXUtWCINDrXvc6DQ8Pq7u7W/39/YrjWPX19ero6NC6deu8DoDv2rVL6XRaJ06ckJndsHKZ7+VcGhoa9MADD0x7GZo1a9Zo69atWrGiOg5QQaKUIwulZE8jiiIvv6PVOAbjPWCScKkezel6bVzCH2mta25uLnpLsJzMTDt27NDmzZt15swZXbx4UVEUqaGhoSwXpJwcu9m0aZOXC2mivObyvRT7nsbGxpL2Spqaqm/D2XvArGtqqZoTBOuCUA9t3FVTF8NEdUqn09qyZUtR57nMVSqVqshYC+aura1N165dK/oQ+ubm5qLXP1u2bNGxY8eK6oILw1BbtxZ/VGqleN8sWlbXoF0r1vqejUxSaoa9pbog1K4Va7WvvbL91wAWr46OjqLbTh6gUax169YVvbcThqHa29uLnnalVGS/+523vWbWQ3Hnoy4I9e6Ou3Rn63qlLFC6IGjqglDpINQDa7frg3fsU8DeC4AyaWxsLCoIzEx1dXVau7b4je0wDLV3795Zx2tSqZT27t1bld2oFdn/3tTcqh/feZ8+8a0nijpcOZRdvzerc06xnEya9iyauiDUd67boYc2JF1fV7Nj2t/XpXOjVxVaoI7mlXrD6g41LIK7XgKoPnfeeaey2awGBgam7c4KgkD19fW67777Sh7cX758ue6//34dPXpUg4ODkpJzaibDpLW1VXv27FFzc/P8P4gHFb3hWO/IoL5w6qieu5zcq6Pw7o31QUqbl67UvvZtem3bRpmZxnITSgWBxnJZffXcy/rG+ROKnZOZlItjbV++Sg9t3K3bW6pv1xDAq4dzTufPn9eJEyc0PDx8/fI/qVRKW7Zs0aZNm5ROz28jd3R09Pp5N5PXOauGy8NU3R0thycy6hsbUibKaUmqXmublqmuiAs5RnGsgcyIcnGs5XUNWpKun3ctAFBO4+PjymazCsNQTU1Ni/6gopkCZkEOUWlO16s5XfplPMIgeNVfLgVAdWtoaCj6OnSLXfWNCgEAFgUCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeFHS1ZTNrF/SaX/lAABqzG3OuWmvXlxSwAAAUCy6yAAAXhAwAAAvCBgAgBcETI0ws4fNzN3icWWBa3vEzHrLPM13mtmnzey4mcVm9vVbtPsJM3vUzM6a2YiZHTOzXzKzumnabjSzvzezq2Y2ZGafNbNN86jRmdnvzfX9C6Vcy8HMfiW/DL45zWu/YGafN7Pz+Ta/dYtpFPU959u+x8wOm9m4mV0ws/9lZtzitootyC2TMS//XtLUlXluIQrx7Psk3S3pgKSZ7j/7XyV9WdKfSxqQtE/S70p6vZJlJUkysyZJX5WUkfRjkpyk35P0NTO70zk34uEzVJ1yLQcz2yLpNyRdvEWTn5A0JOkfJf3UDJMq6ns2s/dJ+rSkT0r6FUmbJf2+pJ2S3lxMzag8Aqb2HHHOnVjoIirgJ5xzsSRNt4Vc4LXOuf6Cn79mZibpt81si3Oua3J6krZI2jm5/MzseUmvSPqgpD8u+yeoTuVaDh+T9FdKVvDTrUd2O+diM0tp5oAp9nv+XUmPOecennzCzC5J+oyZvd0592iRdaOC6CJbZAq60t5kZv9oZsNmNmBmf2pmjVParjWzvzCzS2aWMbPnzexHppnmZjP7y3y3RMbMuszso9O0u8fMHjezUTN7xcxmWrHMaHKlU0S7/mmefib/7/qC594l6UBhODvnuiU9Iendc61zNma208z+wcyumNmYmR0ws4emtNmRb3Mx3/3TY2afya+cZWbNZvY/889n8u2+Yma3z6GkeS8HM3u/pNdK+tVbtSnh+5u1nZm1Sdoq6Z+nvPQv+X+/v5h5ofIImNoTmllqymO67/FTkk5I+neS/kTJluvHJl80syWSHpP0Nkm/pqSr4qikvzSznyxot1nS05LepKQ76iFJvy2pbcr8linpwviUkhXVM5I+ZmYPFjbKh98jc/zsxXpAUizpeMFzuyUdm6btC5J2+SjCzNZJ+qakuyT9jKT3Sroi6Ytm9raCpl9UEoYfkvRWJV1AGX377/NP8u/9bSXdQR+UdERSS8G8HjGzYk5qm9dyMLMV+Xp+2Tl3uYj5lUOU/zc75fkJJV18eypUB0pEF1nt+dY0z31R0vdOee5R59wv5v//r/mVz++Y2Yedc8cl/UdJ2yU96Jz7er7dP5vZGkm/Z2afcM5FSlZqjZLucs6dK5j+J6fMb6mkn3bOfU2SzOwbSlaW75P0tYJ2kb69wig7M7tT0s9L+nPnXF/BS62SBqd5y2VJKzyV8wv5ab+xoDvqUUkvKhk/+Of81vk2Se92zn2u4L2fLvj/GyX9lXPuEwXP/cOUeRW7XOe7HD6iJLgfKaJtWTjnBi25isjeKS+9QZIp+UyoQuzB1J7vl3TvlMd/nqbd3035+W+UfN+vz//8JklnC8Jl0qckrdK3t2bfIukLU8JlOqOT4SJJzrmMkhXRDUcnOedSzrkPzDKtOTGztZL+SdJJJSv3hfYm3dwdFWFLXEMAAARgSURBVEn6a0l3m9kyJQcmdEn6Q0uOiNs+zXSekfSwmf2amXWaWTi1gXPuA845rxuMZna/pB+V9CFX+UuAfFTSe8zsZ8ys1cxep2SPPFKyt4oqRMDUnmPOuYNTHtMN+vfd4ufJcYlWSeened+FgtclaaVuPmptOtNtFWc08xFgZWNmK5UcTWaS3uqcuzalyaCm30K/1RZ9Ocy0jE3SivyK+s2SDkr6A0nH82NcHypo/7OSPi7px5WEzUUz+5P8EWGlms9y+LikT0jqNbMWM2tR0gsS5n+un0M9xfqIpP8j6X8oCeUDSr7vI5p+GaMKEDCL15pb/Hw2/+9lSe3TvK+94HVJuqQbB8urTn5P4EtKwvB7nHNnp2n2gpLxh6l2Kemy8mGmZeyUX6E757qccz+qZM/xHiWHEf/vyXEa59ywc+5XnXPbJHVI+rCSMZ3fnENN81kOdyg5Imyw4PEdSrquBpWMIXnhnMs65z6oZOzvLiW/z7+kpJt3pqPPsIAImMXrvVN+/iElXQlP5X9+TNIGM/uOKe3er+TchsmVzb9K+t5891PVyW/Ff1HJeRFvmeEQ7s9J2ps/f2PyvR1KVpCfu8V75uux/Dw7CuYZSvpBSYedc0OFjV3iiL7dvXfT4LVz7rRz7r8rOSBjLoPb81kOD07zeE7JQQMPSvr7OdRTEufcFefc8/kDDD4gqV7JOVCoQgzy15678wPDUx10zhWecPl2M/uIkoB4vZKt3b9wzr2Sf/0RJYPhnzWzX1fSDfbDyh+llB8rUP59b5f0pJl9WMmRaeslPeScu+mQ5tmYWU7SJ2cbhzGz25SML0nJnklsZu/J//yMc27ythH/T8nK8eclLTGzwoHgkwWHMf+Zkq3+fzKz31CyB/G7ks4o6fopnLfL1/hwER/p9oK6Cv2bkqOtHpb0ZTP7TSUnHv60pB2S3pGf151Kxhf+VsmyDfPvySnZk5GZ7Vey8j8qaVjJUXJ3qeBACzP7hKQfK2IcpqjlkF/+JyX9jnPudyRpmvE6WXIVidTU18ysU8ne1uRG7K6C5fSoc260YD6zfs9m9mYlgXpMSbfrW5Qsy591zp2a5TNjoTjneNTAQ8lKx83waJvS7k1KBryHlXTV/KmkxinTXCvpL5V0g2UkPS/pR6aZ91YlA9OXJI0rWfH8ccHrj0jqneZ9X5f09SnPOUmPzPPzPjxlerO2y7fdpCSQhiRdU3KWeceUNkvy7/3DImqcad6d+TY78/O5ml92B5SE8+Q0VisJiuOSRvPf1WNKxpEm2/yRpMP5aYwoCZqfm1LLI8mfc1G/S8Ush4785/itWab1dUnfnOb5R2ZYNh0F7Yr9nh9QMv50Lb8MnpD0zoX+u+Qx84P7wSwyZvawpP8rabt7dZzxX1Zm9hZJn5e01TlX1uurAa82jMEAN3pASfcY4QLME2MwQAHn3K8vdA3AYkEXGQDAC7rIAABeEDAAAC8IGACAFwQMAMALAgYA4MX/B1EprxXKvne5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aXOM1XCQCBgDKVkNdpW6/eZNuv3nTkjw+XWQAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgqspA0AZGB4e1okTJzQ6OqogCNTc3KwrrrhCYRguWU0EDACsYGNjY9q3b5/OnTsn55ycS74uubu7W88//7yuvPJKdXT4/WKxmRAwALBCjY2N6ZFHHlEmk7kYLBdEUSRJOnLkiMbHx3XdddeVvD7GYABghdq3b9+04ZIviiKdOHFCvb29JawsQcAAwAo0PDx8sVtsLlEU6aWXXipBVZciYABgBTpx4oTiOC64/cDAgCYmJjxWdDkCBgBWoP7+/qLaB0FAwAAAZjcwMKCBgYGi1onjWEFQ2rd8AgYAVph9+/YVvY6ZadWqVR6qmRkBAwAryODgoMbGxoper7q6uuRHMJwHAwArSFdX14zLnJMmo5RiZwqDWOkg0oXzK9PpdGkKzEPAAMAK0tfXd9nvotg0OL5KA+M1ck4yS8ImDJxWV42ooWqUgAEAzG58fPyS+9k40MvnmhTFgVxu1OPCqTHZWDo7WquhiWpde83aUpfKGAwArBT51xpL7ksnzjUpG4cXw+WydRRoMkrp6ZfGp13uEwEDACuEmV1y0crhyUpl40DS7BeydDJ1946qp2/Uc4WXImAAYAVpaWm5+O/+sZoZj1ymiuJYT7/Q7ausaREwALCCdHR0XPyOl8mo8IF756TTPSO+ypoWAQMAK0hTU5NaWloUBIHmvszlpYq5dtliIGAAYAUxM+3atUvr1q1TKiguMCorSvvtlgQMAKwwQRDopptu0pWbm4paL5XiWmQAgAKkUsWdPNnbP6ZMJvJUzeUIGABYoYZHM0W1DwLTyHjWUzXTPF7JHgkAsKiCYPbzX6ZKLh9T3DoLQcAAwAq1cV2tUmHhb+NhaKqpLt01yQgYAFihrt3WLFfgZOUwMN24fW3RRz0LQcAAwApVVZnSDVe1FHQUk0oFunF7aS94ScAAwAp22+5Nat/YoPQMU5ADkyrSod7xxqtK2j0mcbl+AFjRgsD0ltvbdehYv5567ozOnZ9QGARycnJOuqZjjW6+br3qaytLXhsBAwArnJlpR/sa7Whfo8GhcY2MZxQGgdY0VimdKu3Z+/kIGAAoI431VWqsr1rqMiQt04DJxJG6R4eUiSPVV1Spuap2qUsCABRpWQXM4MSovnrqoB7t7pRJMpmyLta66jrdtela7W5uu+TLdpariSirZ/pOamBiVGEQaGvdGrXXNa+I2oHlZnwyq5dPD2liMlJlRai21npVVS6rty7MYNm8SidHBvSRZx/URBQpcvGUZYO678Untf/sCf309tcqsOU5+W0yyuozx/briVeOymTKxJHMTKEFqq+o1Nu37NSulralLhNYEc6PTOqRp0/qyMsDCgKTi50sMMWx05Vtq/VdN21UXU3FUpeJWSyLgDk/Oa6PPPuQRrMzX1dnIs7q2b6T+szR/fqRjptKWF1hJqKs/uiZr+qVsfPKxHkXk3NOkYt1djyrj7/4hAYmR/U9G7YvXaG4zNDQkMbGxhQEgerr61VZWfrZNqXmnNP4+LjiOFZlZaVSqWXxVnDRwNC4PvXFg5qYjOScFEW5kwlzPw8f61fXqXN651t2aPUyGW/A5ZbF/6pvnDmiTDT3FT4zLtZDpw/rro3Xqr5yef2nuu/Ik3pldEgZN/P3M2TiSJ/rekZb6taoo75lxnbwzzmnU6dO6ciRIxofH7/YfRnHsZqbm3X11VeroaFhXtvOZDI6efKkurq6NDExITNTY2OjOjo6tGbNmiXtKp2YmFBXV5e6uroURckRtnNOzc3N2rZtm5qairv8uw9x7PSZrxzWxEQ04znqTtL4ZKRPf+Wwfurt1ysMlmevxre7JX9VYhfra6cPK+MKv4T0H+z/opwr9rvc/BmaHNf+sydmDZcLJuNIXzrxQgmqwkycc/rWt76l5557TiMjI4qiSNlsVtlsVnEcq6enR4899pi6u4v//vLe3l49+OCDOnTokEZGRpTNZpXJZNTb26s9e/bo0Ucf1eTkpIdnNbehoSE9/PDD6uzsVCaTURzHiqLo4nN+8skndfjw4SWpLV/niUGNT84cLvkmJiN1vjzovSbMz5IFjMt1HZ3PTGgiKu7y0UOZcX2u6xkNZ8b15RMH9GfPfU0fefZB3ffikzp+vt9TxTP7Zk8y5lKogwNnNJyZ8FgRZnPkyBF1d3crmuWoOY5j7d+/X0NDQwVvt7+/X3v37lUURdNuO4oiDQ0N6fHHH5/1sX0YHx/XE088cTFYphNFkY4ePaqurq6S1jbV0we6lckW9k2NmWysvQde8VwR5qukXWSxczrQf1r/0rlPPRPnF7StL598QV85+YJCC5TNHTkcOderp3q71FJVp1+49raSTW8+PXKuqCOwVBCqf2JEteny7+tfbqIoUmdnZ0Fv8FEU6ciRI7rpprnH/C4cFc21XeecRkdHdfz4cbW3txdc90IdO3as4Od86NAhtbW1KViibqf+ofGi2g8U2b4cjY+P6+TJkxoZGZGZqba2VmNjYzp9+rQymYyCIFBzc7M6OjrU2NhYsm7akgXMRDaje/Z9Uf0TI4u2TSddDJfkvtNkHOn06Dl9eP+X9f6db1JTVc2iPd5M5vNiFXPEg8Vz5syZotq/8sormpycVEXF7LOVBgcHNTFR2FFpHMfq7OzU1q1bS/KHHsexjh8/PuORy1TOOXV3d+uKK67wXNlMBSzNw65EmUxGzzzzjHp6eiRpxtc4jmN1d3ert7dXa9as0U033aQw9H+Gf0k+osRxrPfv+fdFDZfZODmNZTP6+8OPl+TxttQ2qSIo/MWKXMzJo0tkcHCwqO6pIAg0MjL3/9szZ84Utd1sNqvh4eGC2y/EyMhIUWOWURSpt7fXY0WzK/Ys9Ma6b8+egEwmo0cffVQ9PT2K47igDxBRFOns2bPau3dvScaxSxIwDxx/TkPZ0h7GxnI6PtyvnrGFdcUV4pa1Wwv+0BXItGvNJlUX+V3aWBzz+aMqZJ1iB+7NTNlsab66No7joo+UCj3a8WH3tetmvDLwVOlUoN3Xrvdc0fL07LPPamxsrOjXKo5j9ff3l+RDREkC5sFTB0vxMJeJXawne455f5zqVFq3rd9W0FFMKgh016ZrvNeE6dXW1hY1thDHsaqrq+dsN1cX2lTOOaXTpfmQUVlZWdSbkJkV9Jx96WhrVEW6sB6BinSobW2NnitafiYnJ/XKK6/M+4PAhbFI37wHzJnRcwVN3/Uhcq5k3XJvb9+pqxvXzRoy6SDUT179Wl1R8+33B7FcbNiwoaj2jY2NBb3Ztra2FtWnnU6nVVPjf3xQkqqqqlRfX19wezPTxo0bPVY0uzAI9MPfd7WqKsIZRyrNpMqK5DtOwiK+MrhcnDp1asHb6Ovr895N5v2VOTtWmn7mmVQGpfmUGFqgX7jmdr1ty41qrKhWZZhSVZhWVZhWygLtaFyv33jN92pn86aS1IPpVVRUqLW1taCjmDAMdeWVVxa03cbGxoKvABAEgdrb20t6wuW2bdsKCkAzU0NDg2prl3aMsKmhSj929zXqaGtUGJrSqUCp3M8wNHVsatS7775GaxqX7khrKY2Oji64G9PMvE+X9z6LLFXE4PdiqwxT2r66dP2zgZm+e8PVuuOKq3TsfJ8GJkaVCgK11TZpdeWqktWB2V1//fU6f/68hoeHZ/wjDcNQ7e3tamkp7IoLZqadO3fqm9/85qx/tGammpoabd68eV61z9e6devU2to662QEM1MqldKuXbtKWttM6msr9dY7tmlsPKOu3MUuK9Khtmyo16qqb+8xzMWaQu57Jpn3gNlQM7/LbSyGlAV6TVPpp1qamdrrm0v+uChMKpXS6173Oh04cEAnT5685JNcGIZKpVLavn27Nm0q7mhz9erVuvnmm7Vnzx5JuuyNPAxD1dXV6ZZbbinJFNF8ZqYbbrhBVVVVOnr06GWfXoMgUF1dnXbv3r2k4y/Tqa5Ka0f7mqUuY1lZvXq1wjBc0BFIc7P/K7x7D5j6impdWd+iI0OlnfZYEYT6kfZdy/bKy1haYRjqNa95jXbs2KHu7m6Njo4qCAI1NDSopaVl3n94zc3NuvPOO3Xq1CkdO3bssmuRNTU1Ldm1yMxM27dvV0dHh06ePKm+vj5FUaSamhq1tbUVNU6DpbV27VoFQTDvgAnDUB0dHYtc1eWsmEGe3bt3u7179xb9IC8P9+vD+788r/OnQjPVpao0nJ245KTKmQQypYJAb938Gt25ccc8HhEAlr+uri4dPHiw6JAJw1Br167Vrl27FuXDjpk97ZzbPd2ykny8b6tt0s9uf31B566nLFBFEKoiSKmlqlY/cfVrdc/ut6h1VcOc04BTFmh3S5vee8OdhAuAsrZlyxZt3bq14O5WM1MQBGptbdXOnTtLciRdkiOYC04MD+j/du6dtrvsmob1+o51Wy9+SdeGVQ3aUvfqpc0zcaRHzryk/zh1UMOZCYUWKHKxKoOUbr/iSt2ydouaKmuUXsJJBQBQar29vTpy5IgGBwcvDv7Hcay6ujpNTk4qiiIFQaC1a9eqvb190WcIznYEU9KAuWA4M6ETwwM6nxlXU2WNNtc1FRwMzjn1jJ3XaHZSVam01lXXK+CriAF8mxsfH7/43UY1NTUl+xK52QJmSb5wrDZdqR3znD5sZlq3isFIAMhXVVWlqqrl9UWMTLECAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC+KupqymfVKOu6vHADACrPZOdcy3YKiAgYAgELRRQYA8IKAAQB4QcAAALwgYFYIM3uPmbkZboNLXNu9ZnZykbd5t5n9k5m9aGaxmT08S9vvN7NHzWwgd3vMzH5gmnarzezvzOysmY2Y2YNmdv0Cauwys/vnu/5Sme9+MLOrzOzPzOxZMxs2szNm9oCZ3TBN21Vm9qdmdsrMJszsOTN71wzbrTaze8zsSK7tK2b2eTOryGvz+tz/s+fNLGtmXQvaCSiJJfnKZCzID0ua+maeXYpCPPtBSTdK+qakGb8H1szukvSApM9K+lDu1z8j6V/N7G7n3Bdy7UzSv0vaIumXJQ1Iep+kr5vZjc65RQ3I5WqB++GNku6Q9AlJ+yQ1SvpNSd80s9c7557Oa/tZSbdKer+kw5J+SNL9ZmbOuYuhbGZpSV+StFXSH0p6QVKLpDslhXnb+x5J3yVpryQnqW4+zx8l5pzjtgJukt6j5A9r21LXMk1t90o6ucjbDPL+/aikh2do90+STkgK834XKgnhT+X97gdy+++OvN81SOqX9NF51tgl6f6l3v9F1jzv/SCpWbmZp1PWHZD0ybzfvT73GO+Z0vbzkk5Pea1+W9KQpE1F/H+4X1LXUu9LbnPf6CIrM3ldabeZ2edyXRl9ZvaXZlY9pW2rmX0y11Uykev6+LFptrnVzO4zs+5cu6Nm9mfTtNtpZo+Y2Wiuu+Pn5/s8nHNxgU0rJI0456K8dSNJw7q0C/itkk47576e1+6ckk/zl3WnLZZC9rGZrTezT5jZ6VybM7kuorW55Skz+wMz6zSz8dy2HjWz18+jpHnvB+fcWZd7h5+y7ouSNuT9+jtzP780ZRNfltSat1ySfkHSp51zJ+Z47EL/P2AZIWBWnjD3hpN/m+51vF/SS0q6Jv5USbfRxy4sNLMaSd+Q9CZJv6OkS+o5SfeZ2c/mtdsq6SlJt0n6gKS7JP2ekk+z+eqVHE3cr+SNao+kj5nZHfmNcuF37zyf+3T+RtI2M/tdM2vJ3T6gpAvoL/LaXSvp+WnWPyCpzcxqF7EmSYXvY0n3KelOeq+SrqFfUXIEtiq3/Lck/Zqkj0r6Pkk/IekhSU15j3VPbt9umaOsRd0PZtYk6TpJB/N+fSHsJ6c0n8j9vC63bpukTZKOmtnfmtlQLkAfMrMbi6kDy9RSH0JxK+ymV7vIprt9fpp2/2fK+r+r5A//qtz9X8q1e8OUdg9K6lGuG0PSJ5UcDVwxS2336vJul0pJfZL+ZkrbrKS/L/K5z9hFlhsCEIoAAAVSSURBVFv+ZiXdNBf2x5CkN09p86Kkf55m3Z/OrTNrF80Mj9ulWbrIitjHw5J+ZZbtfF7SZ+eo5QO5fbt5jnaLuh8k/aOkUeV13eZeDyfpTVPafjz3+/fl7n9n3uv1UG69t0l6VtKgpLYZHpMushVy4whm5XmbpJun3P7HNO3+Zcr9f1ZyxHpL7v5tkk455x6e0u5+JYOs1+Tuv1FJgJ2eo65Rd2m3y4SSN7O2/EbOuZRz7qfm2FbBzOw7czV/UcnR1V2SviDp01OPnpZAoft4j6T3mtmvmtn1uYH4fHskvdnMPpSbTVUxZbmcc7+f27clu5STmb1P0o9K+iXn3Et5i76q5Ijmo2Z2qyWz1n5K0jtzyy90d114/xmVdLdz7ovOuX+V9BZJ1ZJ+0fuTgFcEzMrzvHNu75TbS9O0e2WG+xf6ypsknZlmve685ZK0RpfPWpvOwDS/m9AsM8AWyZ8r2Sfvcs59JXd7p6T9kv5kSn2rp1m/KW/5Yit0H/8XJTPhflPJp/dTZvaBvK7PD0v6oJLxk0ck9ZnZP5jZ1G7KQizKfsiNr31Y0vudcx/PX+acy0p6h6QRSY8rmUDwISWz1aRX90lf7udjzrnRvPVPSDokaWchtWD5ImDK17oZ7p/K/eyXtH6a9dbnLZeks7p0AHe5uV7J1NWp9kjakXf/gJLxh6mukfSyc27YQ20F7WPnXI9z7hedcxskbVfS5fh7kn4utzzjnPtfzrnrlQyS/5qkt0v6y3nUtOD9YGbvlvRXkj7inPvQdG2ccy84525UMv34OiVjLReC5bHcz6OSxmZ5KAb2VzgCpnz9yJT7/1XJH+yTufvfkLTRzF43pd2PKhkfeCF3/6uSvt/MWn0VukDdSroJp7pFr4aplBwhbDCz2y/8wszqJd2dW+ZDofv4IufcYefc7yg5krhumuXdzrm/UzKOc9nyAixoP5jZ2yT9g6S/c879xlztnXNdzrkDubu/JOmrzrnO3LKMku7M1+cmRFx4jDYlQbun4GeFZYkTLVeeG2foGtmb65q44M1m9r+VBMQtSrpYPumcO5Jbfq+kX5X0WTP7XSXdYO9SMovp59yr034/qGTw9XEz+7CSmWkbJN3lnLtsSvNczCwr6RNzjcOY2Wa9GhxrJMVm9o7c/T15Yw1/LumPzezCDDZJ+nFJr809vwsekPSEkpP93qtXTzA0SX805bG7lAwiv6GAp9SWV1e+J1TAPjazBiVh8Y9KuoUySmbhrVby2snM/k3SM0pObhxQ0nV0l6S/zqv5A0oG+jvmGIcpZj9c8lqZ2W2SPpWr5d7c+NcFE865/Xnrvk/JV3ucVjIO94u5n1PD9oNKZil+wcw+oqRL9YNKBvn/Im97LZIuhGKbpFV5+/0F59xlYY1lYKlnGXAr7KbZZ5E5Sc1T2t0m6d+UzFDqV9KdUj1lm61KpsieVTJe8qykH5vmsTuUvLGclTQuqVPSn+Qtv1fTnGgp6WFNmf2Vq+3eBT7f90xp+y4lR2YDuduTkt45zTablMxk6lcysPyQpBumaderaWZaTdOua5Ya31HIPlYy2+6vlXRdDSuZUbVH0o/mtfl1JVc06FPSpXRY0j2S0nlt7sk97pYC6i50P1zyWuU9xnS3rinr/k9Jx3LP+RUlZ/9PO0NNyQegr+dqOSfpc5pyQrGkN8zy2Pcs9d8nt+lvfB9MmTGz9yjpwrjSTT/4j1mY2VVK3sC/wzn31FLXA6xkjMEAl7pd0n8QLsDCETBAHufc3zrn3rjUdQDlgC4yAIAXHMEAALwgYAAAXhAwAAAvCBgAgBcEDADAi/8POYl31rgG8GYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QqKGhQU1NTX7q8LJWAEBRLVmyRNu3b1csFruuu8zMFASBlixZoh07dni5yFLiSn4AKFlNTU26//77debMGZ06dUojIyMKgkCNjY1au3atFixY4HX7BAwAlLBEIqGWlha1tLTM+rbpIgMAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDAPCCgAEAeEHAAAC8IGAAAF4QMAAALwgYAIAXBAwAwAsCBgDgBQEDACXKOaeR0bTS6bAo248XZasAAG/OXxrU3tc6dbS9R06SC50a6iq0c8sybVxTr3hsds4tCBgAKCEvvHJWLx3sVDoM5dwbj1+8PKzv7T6pH7x6Vu99cJOqKxPea6GLDABKxL5DXXrpYKdS6WvDZUwyFaq3f0RffuyIkqm093oIGAAoAclkWs/u61BqivEW56QrQ0kdar3ovSYCBgBKwJH2SzLLrW0qFWrPwU65bKc5BUTAAEAJaD3Vq2Qq99li/YNJDY/47SYjYACgBIwm85uKHJhN2Z02UwQMAJSA2ur8ZoWFoVN5WcxTNRECBgBKwK3rG5WI535IX9FUo7IEAQMAmMLKploFOQ7ym0k7Nzf5LUgEDACUhNFkqFQ691lhVRVcaAkAyMGh1m5ZrvOUFV2U6RsBAwAl4LXj3TnPCnNOOnqyx3NFBAwAlIR8r2nJ55qZ6SJgAKAEJBL5Hc5jsdy706aLgAGAErBhVX1eobF62QKP1UQIGAAoAbfdtFjKcRJZIh5ox61MUwYA5KCmqkw7Ni+d8sPE4jHTyqZaNS+t8V4TAQMAJeJNW5t1+6bFiscs652VE/FAq5Yt0DvvXZfXlObp4hMtAaBEmJnu2FSvxpqUXj81oDPnR5RKO8VjplXLFmjH5iY1L6mZlXCRCBgAKAkXL17U4cOH1dfXJzNTpaSNjSYz05o1a7RhwzoFwex2WhEwADDPdXR0aP/+/QrD7Ne2tLa2qru7W7t27VIs5vcGl+MxBgMA81hfX98Nw0WSwjDU5cuXdeDAgVmsjIABgHnt+PHjNwyXMWEY6uzZsxodHZ2FqiIEDADMU6lUSufOnctrmdOnT3uq5noEDADMUz09PXIu91v0h2Govr4+jxVdi4ABgHnIOaf9+/cXu4wbImAAYB7q7e3V8PBw3stVV1d7qCY7AgYA5qEjR45Ma7m6uroCVzI5AgYA5qHe3t5pLcd1MACASaXTaaXT+X3A2Jjy8vICVzM5AgYA5plUKjWt5eLxuGpq/N9FeQwBAwDzTCwWm9YNK1taWmbtRpcSAQMA8048Hs97NpiZaePGjZ4qyo6AAYB5aN26/O6OvGvXrlk9e5EIGACYl5YvX66qqqqc2q5evVqLFi3yXNH1CBgAmIdisZjuuusu1dTUTDr12My0bt06bd68eZari/B5MAAwT5WXl+vuu+/WuXPn1NraevXDxqToDGft2rWzemHlRAQMAMxjQRCoublZzc3NCsNQYRhOe5ZZoREwAFAigiCY9Y9FvpE5FTChczp15ZL6k8MqC+JaXdugilii2GUBAKZhTgRMKkzrybNH9XjHEQ2nkwoUndqlXKg7F6/WO1dtUUPF7N0BFAAwc0UPmNF0Sn9x4Hs6M9Cr0fD6e+u80HVC+7pP67dvu18ra+qLUGH+0mGovuSwnJxqExVKBLN3czkA+UmnQ42MphWPBypL8H+1kIoeMH/z+vM6PdCrZJZwkaRQTkPppD554Hv66I4fU3Vi9m7Ulq/u4Sv6Xsfreq6zVU5vfMrcDy1p0f3Nm9RUtaCI1QEY7+yFK9pzsFNtZy7LTHKh04Kacu3c3KRNaxuUiBM2M1XUgLkwdEUHL51VyoVTtk2mU3quq1U/suKWWagsf4d6zul/H3pGKZdWesJHmD7X2aofnD+hD2y8S3csXlWkCgFI0SdBPrP3jF45ckGp9LXHnt7+ET350mm9eLBTP/PgTaqpKitSlaWhqNMNnjp3VLl+mnTShfp6+6t5ff70bDl15ZI+fehpjYSp68JFis7CRsO0PnP0BR29fL4IFcI355wuXbqkjo4OnT17VoODg3ktm06n5+Tf9kRjtYbh1G8K56qXDnZmDZcxqVSovisj+vJjR5RMTe+W+IgU9Qymre+C0jmcvYxJulD/58hz+tWb3+Kxqvx97cQrWcePJkqGaX2lba8+uO2hWagKs8E5pxMnTqi1tVWpVOrqtQdhGGrhwoXatGmTGhoasi7b09Oj1tZWdXV1XV1XfX291q9fryVLlsyJ6xjGDAwMqK2tTWfOnFEYhnLOqaqqSuvWrVNzc7Pi8aL3tudkZDSt3fvPKpW+cZg7Jw0Mp3So9aJuv2nJLFVXeor2VxE6l/Xd/lT2dp/S4Z5zurl+mSRpOJXUnu6TOjfYJ5O0oqZedzSumrWB9Z6RQR3L46zk3GCfzg70ann1Qo9VYTaEYag9e/bo4sWLWT/86dKlS9q9e7duv/12NTc3X33cOacDBw6oo6PjuuV6enq0b98+1dXV6c4775wTB+5Tp07p4MGDcs5dc5Y1ODioQ4cO6dixY7rrrrtm9bPep+twa7ckk3LoO0mlQu052KnbNi6eU2E/n8zqX+9wOqnnO1v1zVMHNZAanfZ6vty6V3+w/SF9pW2fnutqk0lXzyACM33u6A9097L1eu/aOxR4/sNo6+tWLAiUnOR0O5vjfRcImBJw5MgRdXd337C7KAxD7d+/XzU1NVdv2XHo0KGs4TImnU6rt7dXL730UlHugDveuXPndPDgwUlf49gnKz7//PO65557VFY2t8cs2jouT9o1lk3/YFIjo2lVlBc/6PM1MDCg9vZ2dXd3a3h4WEEQqKqqSi0tLWpqapqVCzJnba+dHejVx17+jpJ5dIlNpnOoT5/Y/7g6Bi9fN/ssdE6S05Nnj2rPhVP6w20Pqa68csbbnEwyTCufEzHnXE7daZjbUqmU2tvbcxqLCMNQx48f1x133KGhoSGdPHlyyuXCMFRvb6+6u7u1ePHiQpWdl7EzrVxe4+joqNra2rRp06ZZqGz6kqn8jj+BKa9AmgsGBwf18ssvq7e397pxvZGREfX29ioej2vHjh3e77A8K4P8PcMD+ui+xwoSLmNuNLV5TH9yWB/e8031jw4XbLsT1ZVV5vUOMx4EqivzF3iYHWfPns3r997V1aXR0VGdPHky52XS6bRaW1unU15BXLhwIefPfXfO5RScxVab56yw0EnlZfPn7GVgYEDPPPOMenp6Jp004pxTMpnUiy++qEuXLnmtZ1YC5rPHdiud83yxqTkp58kBQ2FS//fIcwXb9kQbFy7JqxsudE63NTRP3RBzWm9vb84HXym6R9Tg4KA6OzvzOghfvHhxOuUVRD4BI0VnXVeuXPFY0cxt3tCoRDz3w97q5Qvyal9Mzjm99NJLSiaTObVPp9Pau3ev19mL3vfcaDqlw71dvjdzQ8cun1f3sJ8//JgFetvyjTlNKohboF1LW1Qemz/viFA4Y1N8812mWNOXcz1QjTGzvF/fbFvZVJvzeEo8FmjnrU2eKyqc3t5eDQ0N5bVMKpVSd3e3p4pmIWD2Xsi9S8CXUNILXW3e1v/QylvVXFWnhE2+O+MWaFFFtX6qZZu3OjB7amtr8xokDcNQVVVVeQ+Cx+Pxog3yV1RU5NU+DMM5P8hvZvrxt62b8qwkHgu0eUOjVjTVzlJlM9fe3p53wKfTaZ0+fdpTRbMQMN0jAwVdXyC7ejPMXDk5nR/qL2gd4yWCmH77tvu1ZVGz4hYoPi5oYpmfNy1cqt/b+gB3hy4R46cd52LRokUqLy/X6tWrJ/30wYnMTMuXL59OeQXR3NycV4hWVlbOi6nKSxdV670P3qSaqsR1QROPBYrFTDs2L9Xb7lxZpAqnJ5+Le8cbGRkpcCVv8N5XU1PAe4fFzLRryVq9eKFdYZ4zseKer4spi8X1aze/VT0jg3r63DGdutIjJ6cV1Qt197INaqyo8bp9zK6ysjI1Nzero6NjyjGVWCymDRs2SIoO2q+99lpO2zAztbS0zLjW6aqtrVVtba0uX748ZdtYLKb169fPQlWFsXRRtX7lPbfpdGe/Xj16Qf0Do0rEArWsrNOt6xtVMY8G9sdM90zX57VW3vfi1kUr9aXWvTNeTyDTL930Jt3RuEqHezt1KY8zo/Igrg11s3M1bn15ld615vZZ2RaKa/Pmzerv71dfX9+kIRMEgW666aarV/PHYjFt27ZN+/btu2EwxWIxrV27VrW1xe2i2bZtm5599lmlUqlJ2wRBoPr6eq1YsWIWK5s5M9OqZQu0allp3IR20aJF6u3tzWsSSRAEWrZsmbeavHeR1ZdXaWnlzP6TmKTfue3t2rF4tcxMP9WyNe9ush2N3GQShRWLxXTXXXdpzZo1isViV8dLgiBQLBZTVVWVtm/frrVr116zXFNTk7Zv365YLHZdd9nYJxJu2LBBGzdunM2Xk1VNTY3e/OY3q7Ky8rpax15rU1OT7rzzTq52L7LVq1fnvYyZeQ0Yy2eGyo4dO9yePXvy3khr3wX92f7H815OkhIW6CdaturtzddewPUPJ17WP585POXyZUFMD628Ve9YtXla2wdykU6n1dXVpeHhYZmZFi5cqIULF97woJtOp3Xu3DmdPn1ao6OjisfjWrZsmVasWDHnBsudc7p48aLa29s1MDCgIAjU2Nio1atXq6qqqtjlIWP//v05ddtKUbhs2bJFq1bN7M23me11zu3I+txsBIwk7e46oc8cfSGntoFMiSCm6kSZ3tOyfdJb3D/R8boebds76RU2ZUFMP7SkRT+/fifvrgCUvKnujzfGzHTzzTdfd3Y9HXMiYCTp9JUefeH4izrRf+3FYyZpU91S3bSwSWkXKh7EtLZ2kTbUTX1H2e7hK/p6+6va231KgUUzzFIu1Irqej208hbdvmgF4QLgXw3nnDo6OnTs2DENDg5edx3VihUrtH79etXUFGbi0ZwJmDFXkiM61X9JfclhNZRXqWVB44zvfjyaTqlrqF+pMK268ko1lM/96ZIA4NPAwIBGRkYUBIFqamq8zBi7UcAUZS5eTaJctzQUdmCpLBbXypr6gq4TAOaz6urqol6bND9usgMAmHcIGACAFwQMAMALAgYA4AUBAwDwgoABAHhBwAAAvCBgAABeEDAAAC8IGACAFwQMAMALAgYA4EVed1M2swuSTvorBwAwz6x2zi3O9kReAQMAQK7oIgMAeEHAAAC8IGAAAF4QMPOEmX3AzNwkX71Fru0RMztTwPUtMLMPmdnzZnbRzHoz/353lrb/ycxeyrQbNrPjZvbnZrYoS9u3ZNYzZGadZvZJM6ucZo33Zvb9/dNZvpimux/M7D1m9lUzO5lZ9nUz+7iZ1U5oV2tm/93MnjKzvsx+uneSdTaa2d+a2YXMOn9gZg9kafcL47btzOyR6b5+zB4CZv75aUl3Tfiadwe5KayS9BuSvi/pfZJ+RtJRSV8zs383oW2DpH+Q9AFJD0r6lKRfkvS4mV39+zaz2yQ9Lum8pHdK+gNJvyjpEY+vY86Z4X74HUlpSb+vaF9/WtLDmrCvJS1S9DtIZbY1WS3lkp7IrOt3Jf2kpNOSvpklkN4naV1mfX051Iq5wDnH1zz4UnQAdZLWF7uWLLU9IulMAddXLakqy+Pfk3Qqh+V/LbOv7hj32NckHZOUGPfY+zPttk+jxnszy95f7P2fZ93T3g+SFmd5bGzZ+8Y9ZuP+fX/m+XuzLPu+ic9JMkmvSnpxQttg3L/PSHqk2PuSr6m/OIMpMeO60u42s380syuZ7qNPTewGMbNlZvY5M+s2sxEze9XM3pdlnS1m9vlMd8qImbWZ2V9mabfNzJ4xs0EzO2Zmvz6d1+CcG3DODWZ5ao+k5Tms4mLmeypTV0LRu+RHnXPJce0elTQq6V3TqTMXZlVhyoYAAAcoSURBVPagmb2Q6f65nPmd3DShzQOZLqvLmd/X62b2oXHPbzSzr5nZ+Uw34Ckz+4qZxfOsZUb7wTl3IcvDL2W+N49rl+u1D7skDTnnnpqw7Hcl7TSz8esMc1wn5hACZv6JmVl8wle23+PfSTquqNvhLyT9iqIuDUmSmVUr6oJ6SFGXx7slHZD0eTP71XHtWiS9KOluSR9SdID6r5IaJ2xvgaQvZrb7LkUHnk+b2dvGN5ph//ndko5keyKzH6rMbFemvu855/Znnl4nqULSwfHLOOeGJbVKumWa9dyQmT0o6VuSrijq5ntY0mZJz44dPM1sraSvSzqRafPjkj6p6CxuzLcUHcAflvSApP8iaUTj/v+aWbuZPTVFST72wz2Z74ensWxaUjLL4yOZ75unsU7MIXm9A8KckO0A+y1F/enjfds59zuZf3/XzJykPzKzjznnjirqd98g6W3j3kE+ZmZLJX3UzP7GOZdWdLCulHS7c+7suPV/dsL2aiX9hnPuSUkys6cVHQz/jaQnx7VLZ77ykgm9XYq6VSY+VyOpf9xD/6xorGpMQ+Z7T5ZVXxr3fKF9VFKbpIecc2NnUy8oGk/6bUm/JWm7pDJJDzvnxsYWnhhbgZk1Slov6V3Oua+PW/cXJ2wrpan3a0H3QyYk/0jSvzjn9uSzbMbrkhaY2c3OufEBdVfmu6/fC2YJZzDzz09I2jnh6z9maffohJ+/pOj3fWfm57sldYzvnsj4O0mL9ca72R+R9M0J4ZLN4Fi4SJJzbkTRgXTV+EbOubhz7penWNc1MgO+/1PS55xzX8i2bUX74a2SflPSVknfyLcLqZAyZ4jbJX15LFwkyTl3QtJzeuOd/yuK3sV/KTNLa8mEVV1UFFJ/ama/YmYbsm3PObfeOff2Qr+OyWRC/Z8UBdsvTnM1X5TULemzZrYlM6Ps9xX9bUoS3WLzHAEz/xx0zu2Z8HU8S7uuSX4e69dukHQuy3Kd456XohlBuUxBzvaueERRl8y0mdlORV1IT0j6t9naOOfCzH541jn3V5J+VtEB/D0TaqvPsniDonfvhVavaMB6sn3cIEmZ390Div4vfl5Sp5ntNrN7Ms87ST+saPzp45KOZsbAHp5GTQXZD5mxvG9IWivpAefctKaoO+d6FXXhNioa2L+gaPbZRzJNsu07zCMETOlaOsnPHZnvlyQ1ZVmuadzzUvQOszlLO+/MbIui7q5XJP3UhIHpGxnrrlmf+d6qKOxunbD+CkUHyUMzr/Y6PYpmSE22j68ezJ1zTzrnHpS0UNGsq5Skb2W6x+Sca3POvV/RmeU2RWH712b2UJ41zXg/ZCYK/L2kHZLe4Zw7kGcN13DOPaNobGijpJsz35OShiTtncm6UXwETOl674Sff1ZRl8MPMj9/X9IKM3vzhHY/p+gaibGDzXclvdPMlvkqNJtMV9DjirqH3umcG8pj8bHup1ZJcs6NSvqOpPdO6DZ7j6RyRWdIBeWcG1B0gPxpM4uNPW5mqyW9SdJTWZYZcc49IenPFA3yt0x43jnnXlE0diPlOQg+0/2QmUzyBUn3SXq3c253Ptu/QV3OOXfMOXdEUpWiCSmfz+xDzGMM8s8/W8fe2U6wZ3xfv6R3mNknFAXEnZI+rGgM41jm+Uck/QdJ/2BmH1TUDfbzirpjfi0zwK/Mcu+Q9LyZfUzRzLRmSQ86564bcJ+KmaUkffZG4zCZcYjHFQ1+f1jSLWY2vsnLzrkRM6tTdMD8gqJrO1zmtf6WpP2KLsAc8xFJuyU9amafkrRG0ick/b1z7uo75cx4z5OSftE590gOL+mtZrZwwmMp59w/SvpDRRMwvmlmfy2pRtGkicuS/jyzvV9XNObwbUUXGTZK+j1JZyUdzFwY+ZeSvqxo38cUXROV0rWTAY5LOpnDOEyu++H9kv5W0tudc9/PPPwpRZMn/kTSQGbG3pgz47vKMmdX1ZK2ZB66J/N3O+Cce2xcu48rCuJuRWec/1nRGczvjS/azG7RG+OClZJWm9lYF+j3J5lCjWIr9oU4fOX2pTcutJzsq3FCu7sVDcJeUdQd8ylJlRPWuUxRv3+3oq6TVyW9L8u210n6f5l2Y1NaPznu+UeU5UJLRe/Sn5rwmNMUF8npjYsYJ/tak2lXruggeFTSgKID935JH5RUm2W9d0t6IfMauiT9D024oFPSj2a28eAMarwyrt2DmW0OZer7J0k3jXv+rsxjpzO/g3OSvjLWRtISRTP2jiqazHBJ0dnnAxPqaZ+4r29Qey77Yezv6N4J25jsNX8kSz3Z2rVPaPe3it7cjGa+/5Wkhiw1f+QG2743l9fN1+x/8XkwJcbMPiDpM5I2uOyD/7iBzFnaj0va4vjPAcwIYzDAte6R9DHCBZg5xmCAcZxzEyc9AJgmusgAAF7QRQYA8IKAAQB4QcAAALwgYAAAXhAwAAAv/j88YkmHZcJKIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "model = GCN()\n",
    "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Define optimizer.\n",
    "\n",
    "def train(data):\n",
    "    optimizer.zero_grad()  # Clear gradients.\n",
    "    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.\n",
    "    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\n",
    "    loss.backward()  # Derive gradients.\n",
    "    optimizer.step()  # Update parameters based on gradients.\n",
    "    return loss, h\n",
    "\n",
    "for epoch in range(401):\n",
    "    loss, h = train(data)\n",
    "    # Visualize the node embeddings every 10 epochs\n",
    "    if epoch % 10 == 0:\n",
    "        visualize(h, color=data.y, epoch=epoch, loss=loss)\n",
    "        time.sleep(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F2B3X6tf9YpS"
   },
   "source": [
    "As one can see, our 3-layer GCN model manages to linearly separating the communities and classifying most of the nodes correctly.\n",
    "\n",
    "Furthermore, we did this all with a few lines of code, thanks to the PyTorch Geometric library which helped us out with data handling and GNN implementations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E9bELRjibIRO"
   },
   "source": [
    "## Documentation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OmqyWVNObNcK"
   },
   "source": [
    "You can explore more PyG functions through its [documentation](https://pytorch-geometric.readthedocs.io/en/latest/)."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "CS224W - Colab 0.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
